pacman::p_load(sp, sf, sfdep, tmap, stplanr, tidyverse, skimr, knitr, DT, performance, reshape2, h3jsr, ggpubr, ggplot2, plotly,httr)Take-home_Ex2: Applied Spatial Interaction Models: A case study of Singapore public bus commuter flows
Setting the Scene and Objective
What are the driving forces behind urban dwellers to weak up early in morning to commute from their home locations to their work places? What are the impact of removing a public bus service on the commuters reside along the corridor of the bus route? These and many other questions related to urban mobility are challenges faced by transport operators and urban managers.
To provide answer to this question, traditionally, commuters survey will be used. However, commuters survey is very costly, time-consuming and laborous, not to mention that the survey data tend to take a long time to clean and analyse. As a result, by the time the survey report was ready, most of the information already out-of-date!
As city-wide urban infrastructures such as public buses, mass rapid transits, public utilities and roads become digital, the data sets obtained can be used as a framework for tracking movement patterns through space and time. This is particularly true with the recent trend of massive deployment of pervasive computing technologies such as GPS on the vehicles and SMART cards used by public transport commuters.
Unfortunately, this explosive growth of geospatially-referenced data has far outpaced the planner’s ability to utilize and transform the data into insightful information thus creating an adverse impact on the return on the investment made to collect and manage this data.
The objective of this exercise is to conduct a case study to demonstrate the potential value of GDSA to integrate publicly available data from multiple sources for building a spatial interaction models to determine factors affecting urban mobility patterns of public bus transit.
The Data
Open Government Data
For the purpose of this assignment, data from several open government sources will be used:
Passenger Volume by Origin Destination Bus Stops, Bus Stop Location, Train Station and Train Station Exit Point, just to name a few of them, from LTA DataMall.
Master Plan 2019 Subzone Boundary, HDB Property Information, School Directory and Information and other relevant data from Data.gov.sg.
Specially collected data
Business, entertn, F&B, FinServ, Leisure&Recreation and Retails are geospatial data sets of the locations of business establishments, entertainments, food and beverage outlets, financial centres, leisure and recreation centres, retail and services stores/outlets I compiled for urban mobility study. They are available on in the geospatial folder to Take-home Exercise 2 data folder.
HDB: This data set is the geocoded version of HDB Property Information data from data.gov.
These are data sets assembled by course teaching personnel.
The Task
The specific tasks of this take-home exercise are as follows:
Geospatial Data Science
Derive an analytical hexagon data of 375m (this distance is the perpendicular distance between the centre of the hexagon and its edges) to represent the traffic analysis zone (TAZ).
With reference to the time intervals provided in the table below, construct an O-D matrix of commuter flows for a time interval (Weekday morning peak is selected for this exercise) by integrating Passenger Volume by Origin Destination Bus Stops and Bus Stop Location from LTA DataMall. The O-D matrix must be aggregated at the analytics hexagon level
Peak hour period Bus tap on time Weekday morning peak 6am to 9am Weekday afternoon peak 5pm to 8pm Weekend/holiday morning peak 11am to 2pm Weekend/holiday evening peak 4pm to 7pm Display the O-D flows of the passenger trips by using appropriate geovisualisation methods (not more than 5 maps).
Describe the spatial patterns revealed by the geovisualisation (not more than 100 words per visual).
Assemble at least three propulsive and three attractiveness variables by using aspatial and geospatial from publicly available sources.
Compute a distance matrix by using the analytical hexagon data derived earlier.
Spatial Interaction Modelling
Calibrate spatial interactive models to determine factors affecting urban commuting flows at the selected time interval.
Present the modelling results by using appropriate geovisualisation and graphical visualisation methods. (Not more than 5 visuals)
With reference to the Spatial Interaction Model output tables, maps and data visualisation prepared, describe the modelling results. (not more than 100 words per visual).
Libraries Used
The following libraries will be used:
Getting Started
Importing Aspatial data
bus_aug <- read_csv("data/aspatial/origin_destination_bus_202308.csv")Rows: 5709512 Columns: 7
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (5): YEAR_MONTH, DAY_TYPE, PT_TYPE, ORIGIN_PT_CODE, DESTINATION_PT_CODE
dbl (2): TIME_PER_HOUR, TOTAL_TRIPS
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.summary(bus_aug) YEAR_MONTH DAY_TYPE TIME_PER_HOUR PT_TYPE
Length:5709512 Length:5709512 Min. : 0.00 Length:5709512
Class :character Class :character 1st Qu.:10.00 Class :character
Mode :character Mode :character Median :14.00 Mode :character
Mean :14.07
3rd Qu.:18.00
Max. :23.00
ORIGIN_PT_CODE DESTINATION_PT_CODE TOTAL_TRIPS
Length:5709512 Length:5709512 Min. : 1.00
Class :character Class :character 1st Qu.: 2.00
Mode :character Mode :character Median : 4.00
Mean : 21.04
3rd Qu.: 13.00
Max. :35049.00 Note that the ORIGIN_PT_CODE and DESTINATION_PT_CODE fields are character fields and we will need to convert to factor field.
bus_aug$DESTINATION_PT_CODE <- as.factor(bus_aug$DESTINATION_PT_CODE)
bus_aug$ORIGIN_PT_CODE <- as.factor(bus_aug$ORIGIN_PT_CODE)We will now compute the sum of all trips for Weekday morning peak.
sum_trips_am = bus_aug %>%
filter(TIME_PER_HOUR >= 6 & TIME_PER_HOUR <= 9) %>%
filter(DAY_TYPE == 'WEEKDAY') %>%
group_by(ORIGIN_PT_CODE, DESTINATION_PT_CODE) %>%
summarise(TRIPS = sum(TOTAL_TRIPS))`summarise()` has grouped output by 'ORIGIN_PT_CODE'. You can override using
the `.groups` argument.datatable(sum_trips_am)Warning in instance$preRenderHook(instance): It seems your data is too big for
client-side DataTables. You may consider server-side processing:
https://rstudio.github.io/DT/server.htmlImporting Geospatial data
busstop <- st_read(dsn="data/geospatial", layer = "BusStop") %>%
st_transform(crs = 3414)Reading layer `BusStop' from data source
`C:\jjwoo\WOO JIA JIAN (clean by Prof Kam)\Take-home_Ex2\data\geospatial'
using driver `ESRI Shapefile'
Simple feature collection with 5161 features and 3 fields
Geometry type: POINT
Dimension: XY
Bounding box: xmin: 3970.122 ymin: 26482.1 xmax: 48284.56 ymax: 52983.82
Projected CRS: SVY21Note that the raw data is in WGS84 geographic coordinate system, we will convert to EPSG 3414 to suit the projected coordinate system for Singapore context.
It is a good practice to check for duplicates.
duplicate <- busstop %>%
group_by(BUS_STOP_N) %>%
filter(n()>1) %>%
ungroup() %>%
arrange(BUS_STOP_N)datatable(duplicate)From the output, we can see some duplicates caused by discrepancies in loc_desc field. We will remove duplicates as this might cause issues when we map these busstops later on.
busstop <- busstop %>%
distinct(BUS_STOP_N,.keep_all = TRUE)duplicate <- busstop %>%
group_by(BUS_STOP_N) %>%
filter(n()>1) %>%
ungroup() %>%
arrange(BUS_STOP_N)Nice, the duplicated records for busstops are removed.
mpsz <- st_read(dsn='data/geospatial',
layer='MPSZ-2019') %>%
st_transform(crs=3414)Reading layer `MPSZ-2019' from data source
`C:\jjwoo\WOO JIA JIAN (clean by Prof Kam)\Take-home_Ex2\data\geospatial'
using driver `ESRI Shapefile'
Simple feature collection with 332 features and 6 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: 103.6057 ymin: 1.158699 xmax: 104.0885 ymax: 1.470775
Geodetic CRS: WGS 84Hexagon layer
We will now create an analytical hexagon grid of 375m. This will be done using sf package.
We will create hexagons of 750 in cellsize, since we want to have 375 from centre to edge.
area_hex_grid = st_make_grid(busstop,
cellsize= 750,
what = "polygons",
square = FALSE)
hex_grid_sf = st_sf(area_hex_grid) %>%
mutate(grid_id = 1:length(lengths(area_hex_grid)))
hex_grid_sf$num_of_bs = lengths(st_intersects(hex_grid_sf, busstop))
bs_count = filter(hex_grid_sf, num_of_bs > 0)
bs_countSimple feature collection with 834 features and 2 fields
Geometry type: POLYGON
Dimension: XY
Bounding box: xmin: 3595.122 ymin: 26049.09 xmax: 48595.12 ymax: 53545.39
Projected CRS: SVY21 / Singapore TM
First 10 features:
area_hex_grid grid_id num_of_bs
1 POLYGON ((3970.122 27348.13... 23 1
2 POLYGON ((4345.122 27997.65... 44 1
3 POLYGON ((4345.122 30595.72... 46 2
4 POLYGON ((4720.122 28647.16... 66 1
5 POLYGON ((4720.122 29946.2,... 67 4
6 POLYGON ((4720.122 31245.24... 68 1
7 POLYGON ((5095.122 27997.65... 86 1
8 POLYGON ((5095.122 29296.68... 87 1
9 POLYGON ((5095.122 30595.72... 88 4
10 POLYGON ((5095.122 31894.76... 89 2We will now assign “grid_id” to every busstop. The code below will overlap points and hexagon polygon using st_intersection().
busstop_hex = st_intersection(busstop, bs_count) %>%
select(BUS_STOP_N, LOC_DESC, grid_id, num_of_bs)Warning: attribute variables are assumed to be spatially constant throughout
all geometriesbusstop_hexSimple feature collection with 5145 features and 4 fields
Geometry type: POINT
Dimension: XY
Bounding box: xmin: 3970.122 ymin: 26482.1 xmax: 48284.56 ymax: 52983.82
Projected CRS: SVY21 / Singapore TM
First 10 features:
BUS_STOP_N LOC_DESC grid_id num_of_bs geometry
3261 25059 AFT TUAS STH BLVD 23 1 POINT (3970.122 28063.28)
2565 25751 BEF TUAS STH AVE 14 44 1 POINT (4427.939 28758.67)
254 26379 YONG NAM 46 2 POINT (4473.292 30681.57)
2398 26369 SEE HUP SENG 46 2 POINT (4604.363 31212.96)
2824 25741 HALLIBURTON 66 1 POINT (5060.733 29212.4)
1714 26399 AFT TUAS VIEW CRES 67 4 POINT (5079.51 30214.42)
2822 25719 THE INDEX 67 4 POINT (4799.476 30158.46)
2823 25711 OPP THE INDEX 67 4 POINT (4831.438 30132.43)
4189 26389 BEF TUAS STH AVE 5 67 4 POINT (4776.694 30324.88)
1564 26299 BEF TUAS STH AVE 6 68 1 POINT (4879.489 31948.93)datatable(busstop_hex)Warning in instance$preRenderHook(instance): It seems your data is too big for
client-side DataTables. You may consider server-side processing:
https://rstudio.github.io/DT/server.htmlO-D matrix of commuter flows for Weekday morning peak
We will now join the sum_trips_am and busstop_hex tables to get the number of trips between OD hexagon pairs.
od_data <- left_join(sum_trips_am, busstop_hex,
by = c("ORIGIN_PT_CODE" = "BUS_STOP_N")) %>%
rename(ORIGIN_BS = ORIGIN_PT_CODE,
ORIGIN_GRID_ID = grid_id,
DESTIN_BS = DESTINATION_PT_CODE,
ORIGIN_LOC_DESC= LOC_DESC)The join was done on origin busstop, now we will do the same for destination busstop.
od_data <- left_join(od_data, busstop_hex,
by = c("DESTIN_BS" = "BUS_STOP_N")) %>%
rename(DESTIN_GRID_ID = grid_id,
DESTIN_LOC_DESC= LOC_DESC,
num_of_bs_origin = num_of_bs.x,
num_of_bs_destin = num_of_bs.y) We will further clean up on od_data now as there are missing grid_id. These will be dropped. We will also create a new weekday_morning_peak to show the sum of all trips between od pairs.
od_data <- od_data %>%
group_by(ORIGIN_GRID_ID, DESTIN_GRID_ID) %>%
summarise(MORNING_PEAK = sum(TRIPS),
ORIGIN_DESC = paste(unique(ORIGIN_LOC_DESC), collapse = ', '),
DESTIN_DESC = paste(unique(DESTIN_LOC_DESC), collapse = ', ')) %>%
ungroup() %>%
drop_na() `summarise()` has grouped output by 'ORIGIN_GRID_ID'. You can override using
the `.groups` argument.Distance matrix by hexagon level
We will use as.Spatial() to convert bs_count from sf data frame to SpatialPolygonsDataFrame.
bs_count_sp <- as(bs_count, "Spatial")Then we will use spDists() to compute the distance between the hexagon centroids.
dist = sp::spDists(bs_count_sp)We will attach grid_id to rows and cols for further matching later.
grid_id_names = bs_count$grid_id
colnames(dist) = paste0(grid_id_names)
rownames(dist) = paste0(grid_id_names)Using the melt() function to convert wide-format to long-format data frame with columns for each row and col with their respective values.
distPair <- reshape2::melt(dist) %>%
rename(dist = value)Now we will filter distPair for distances of 0.
distPair %>%
filter(dist > 0) Var1 Var2 dist
1 44 23 750.000
2 46 23 3269.174
3 66 23 1500.000
4 67 23 2704.163
5 68 23 3968.627
6 86 23 1299.038
7 87 23 2250.000
8 88 23 3436.932
9 89 23 4683.748
10 90 23 5952.940
11 109 23 3000.000
12 110 23 4175.823
13 111 23 5408.327
14 112 23 6666.146
15 128 23 1984.313
16 129 23 2704.163
17 130 23 3750.000
18 131 23 4918.079
19 132 23 6139.015
20 133 23 7386.643
21 134 23 8649.422
22 150 23 2598.076
23 151 23 3436.932
24 152 23 4500.000
25 153 23 5662.376
26 154 23 6873.864
27 155 23 8112.490
28 156 23 9367.497
29 172 23 4175.823
30 174 23 6408.003
31 175 23 7611.669
32 176 23 8842.370
33 195 23 6000.000
34 196 23 7154.544
35 197 23 8351.647
36 215 23 5662.376
37 216 23 6750.000
38 217 23 7901.740
39 237 23 6408.003
40 238 23 7500.000
41 239 23 8649.422
42 257 23 6139.015
43 258 23 7154.544
44 259 23 8250.000
45 278 23 5952.940
46 279 23 6873.864
47 280 23 7901.740
48 298 23 5857.687
49 299 23 6666.146
50 300 23 7611.669
51 320 23 6538.348
52 321 23 7386.643
53 322 23 8351.647
54 340 23 6495.191
55 341 23 7232.738
56 342 23 8112.490
57 363 23 7937.254
58 364 23 8842.370
59 383 23 7830.230
60 384 23 8649.422
61 385 23 9575.359
62 404 23 7794.229
63 405 23 8518.363
64 406 23 9367.497
65 407 23 10310.795
66 408 23 11324.752
67 425 23 8452.071
68 426 23 9216.154
69 427 23 10090.218
70 428 23 11048.190
71 429 23 12070.108
72 446 23 8452.071
73 447 23 9124.144
74 448 23 9921.567
75 449 23 10816.654
76 450 23 11787.175
77 452 23 13890.194
78 468 23 9807.523
79 469 23 10633.085
80 470 23 11546.103
81 471 23 12527.470
82 472 23 13562.356
83 488 23 9124.144
84 489 23 9750.000
85 490 23 10500.000
86 491 23 11349.559
87 493 23 13268.855
88 494 23 14309.088
89 509 23 9750.000
90 510 23 10419.333
91 511 23 11199.888
92 512 23 12070.108
93 513 23 13012.014
94 514 23 14011.156
95 515 23 15056.145
96 530 23 9807.523
97 531 23 10392.305
98 532 23 11098.986
99 533 23 11905.881
100 534 23 12794.042
101 536 23 14754.237
102 537 23 15803.481
103 538 23 16887.495
104 539 23 18000.000
105 551 23 10419.333
106 552 23 11048.190
107 553 23 11787.175
108 554 23 12616.953
109 555 23 13520.817
110 559 23 17636.964
111 560 23 18750.000
112 561 23 19885.610
113 572 23 10500.000
114 573 23 11048.190
115 574 23 11715.375
116 575 23 12482.488
117 576 23 13332.292
118 578 23 15223.337
119 582 23 19500.000
120 583 23 20635.225
121 584 23 21788.759
122 593 23 11098.986
123 594 23 11691.343
124 595 23 12392.034
125 596 23 13183.797
126 597 23 14051.245
127 603 23 20250.000
128 604 23 21384.866
129 615 23 11715.375
130 616 23 12346.558
131 617 23 13076.697
132 618 23 13890.194
133 620 23 15714.245
134 637 23 13012.014
135 638 23 13768.170
136 657 23 12392.034
137 658 23 12990.381
138 659 23 13686.216
139 660 23 14465.476
140 662 23 16224.981
141 677 23 12482.488
142 678 23 13012.014
143 679 23 13645.054
144 680 23 14367.933
145 681 23 15167.811
146 699 23 13076.697
147 700 23 13645.054
148 701 23 14309.088
149 702 23 15056.145
150 704 23 16753.731
151 722 23 14981.238
152 723 23 15750.000
153 725 23 17476.770
154 730 23 22537.469
155 741 23 13768.170
156 743 23 14943.644
157 744 23 15660.460
158 752 23 23286.262
159 761 23 13890.194
160 762 23 14367.933
161 763 23 14943.644
162 764 23 15606.489
163 765 23 16345.871
164 767 23 18015.618
165 772 23 22957.842
166 784 23 14981.238
167 785 23 15588.457
168 786 23 16276.901
169 787 23 17036.725
170 788 23 17858.821
171 789 23 18734.994
172 803 23 14600.942
173 804 23 15056.145
174 805 23 15606.489
175 806 23 16242.306
176 807 23 16953.982
177 808 23 17732.386
178 809 23 18569.128
179 810 23 19456.683
180 814 23 23394.711
181 819 23 28882.304
182 824 23 14773.287
183 826 23 15660.460
184 827 23 16242.306
185 828 23 16904.142
186 829 23 17636.964
187 830 23 18432.309
188 831 23 19282.440
189 832 23 20180.436
190 835 23 23104.383
191 844 23 14981.238
192 846 23 15750.000
193 847 23 16276.901
194 848 23 16887.495
195 849 23 17573.062
196 850 23 18325.188
197 851 23 19136.026
198 852 23 19998.437
199 853 23 20906.040
200 854 23 21853.203
201 855 23 22835.006
202 856 23 23847.170
203 866 23 15497.984
204 867 23 15874.508
205 868 23 16345.871
206 869 23 16904.142
207 870 23 17541.023
208 871 23 18248.288
209 872 23 19018.083
210 873 23 19843.135
211 874 23 20716.841
212 875 23 21633.308
213 876 23 22587.331
214 877 23 23574.350
215 887 23 16033.169
216 888 23 16448.784
217 889 23 16953.982
218 890 23 17541.023
219 891 23 18201.992
220 893 23 19715.159
221 894 23 20553.284
222 895 23 21437.409
223 896 23 22362.077
224 897 23 23322.468
225 898 23 24314.348
226 908 23 16224.981
227 909 23 16585.008
228 910 23 17036.725
229 911 23 17573.062
230 912 23 18186.533
231 915 23 20415.986
232 917 23 22159.930
233 918 23 23092.206
234 919 23 24058.522
235 928 23 16448.784
236 929 23 16753.731
237 930 23 17151.895
238 931 23 17636.964
239 932 23 18201.992
240 933 23 18839.785
241 934 23 19543.221
242 935 23 20305.480
243 938 23 22884.219
244 939 23 23823.570
245 940 23 24795.413
246 942 23 26822.332
247 949 23 16703.293
248 950 23 16953.982
249 951 23 17298.844
250 952 23 17732.386
251 953 23 18248.288
252 954 23 18839.785
253 955 23 19500.000
254 956 23 20222.203
255 957 23 21000.000
256 959 23 22699.119
257 961 23 24556.058
258 962 23 25533.067
259 970 23 17184.659
260 971 23 17476.770
261 972 23 17858.821
262 973 23 18325.188
263 974 23 18869.618
264 975 23 19485.572
265 976 23 20166.494
266 977 23 20906.040
267 978 23 21698.214
268 982 23 25289.573
269 983 23 26271.420
270 984 23 27279.800
271 991 23 17444.555
272 992 23 17684.739
273 993 23 18015.618
274 994 23 18432.309
275 995 23 18929.144
276 996 23 19500.000
277 997 23 20138.582
278 998 23 20838.666
279 999 23 21594.270
280 1001 23 23250.000
281 1003 23 25066.162
282 1004 23 26024.027
283 1011 23 17732.386
284 1012 23 17921.705
285 1013 23 18201.992
286 1014 23 18569.128
287 1015 23 19018.083
288 1016 23 19543.221
289 1018 23 20798.137
290 1019 23 21515.982
291 1023 23 24863.377
292 1024 23 25796.075
293 1025 23 26759.344
294 1033 23 18186.533
295 1034 23 18417.044
296 1035 23 18734.994
297 1036 23 19136.026
298 1037 23 19615.045
299 1043 23 23811.762
300 1045 23 25588.083
301 1046 23 26527.109
302 1053 23 18478.027
303 1054 23 18659.783
304 1055 23 18929.144
305 1056 23 19282.440
306 1064 23 24521.674
307 1066 23 26314.207
308 1067 23 27259.173
309 1074 23 18794.946
310 1075 23 18929.144
311 1076 23 19150.718
312 1077 23 19456.683
313 1079 23 20305.480
314 1085 23 24383.652
315 1087 23 26121.112
316 1088 23 27041.635
317 1094 23 19136.026
318 1095 23 19224.008
319 1096 23 19398.776
320 1097 23 19658.014
321 1098 23 19998.437
322 1099 23 20415.986
323 1105 23 24268.035
324 1106 23 25088.593
325 1107 23 25948.266
326 1108 23 26843.295
327 1109 23 27770.263
328 1116 23 19543.221
329 1117 23 19672.316
330 1118 23 19885.610
331 1119 23 20180.436
332 1120 23 20553.284
333 1129 23 26664.583
334 1130 23 27566.964
335 1131 23 28500.000
336 1136 23 19885.610
337 1138 23 20138.582
338 1139 23 20388.416
339 1141 23 21120.192
340 1148 23 25664.908
341 1149 23 26505.896
342 1150 23 27382.704
343 1151 23 28292.004
344 1157 23 20250.000
345 1158 23 20291.624
346 1159 23 20415.986
347 1160 23 20621.591
348 1171 23 27217.871
349 1172 23 28102.491
350 1173 23 29018.313
351 1174 23 29962.477
352 1178 23 20635.225
353 1179 23 20716.841
354 1180 23 20879.116
355 1181 23 21120.192
356 1183 23 21827.448
357 1190 23 26250.000
358 1192 23 27931.837
359 1193 23 28823.818
360 1194 23 29745.798
361 1199 23 21000.000
362 1200 23 21040.140
363 1201 23 21160.104
364 1203 23 21633.308
365 1204 23 21981.526
366 1211 23 26153.394
367 1214 23 28647.644
368 1215 23 29546.573
369 1216 23 30474.374
370 1220 23 21384.866
371 1221 23 21463.632
372 1222 23 21620.303
373 1223 23 21853.203
374 1224 23 22159.930
375 1231 23 26078.008
376 1232 23 26843.295
377 1235 23 29365.158
378 1236 23 30270.654
379 1241 23 21750.000
380 1242 23 21788.759
381 1243 23 21904.623
382 1246 23 22699.119
383 1256 23 29201.884
384 1257 23 30084.257
385 1258 23 30995.967
386 1262 23 22134.532
387 1263 23 22210.639
388 1264 23 22362.077
389 1265 23 22587.331
390 1266 23 22884.219
391 1267 23 23250.000
392 1272 23 25991.585
393 1273 23 26696.208
394 1277 23 29915.506
395 1278 23 30804.829
396 1283 23 22500.000
397 1284 23 22537.469
398 1285 23 22649.503
399 1286 23 22835.006
400 1289 23 23811.762
401 1293 23 25980.762
402 1294 23 26654.033
403 1295 23 27372.431
404 1298 23 29764.702
405 1299 23 30630.867
406 1304 23 22884.219
407 1305 23 22957.842
408 1307 23 23322.468
409 1308 23 23610.114
410 1310 23 24383.652
411 1316 23 28052.406
412 1317 23 28823.818
413 1318 23 29632.119
414 1319 23 30474.374
415 1320 23 31347.847
416 1324 23 23286.262
417 1325 23 23250.000
418 1326 23 23286.262
419 1327 23 23394.711
420 1328 23 23574.350
421 1329 23 23823.570
422 1330 23 24140.215
423 1331 23 24521.674
424 1333 23 25466.890
425 1334 23 26024.027
426 1335 23 26632.921
427 1336 23 27290.108
428 1337 23 27992.186
429 1338 23 28735.866
430 1339 23 29518.003
431 1340 23 30335.623
432 1341 23 31185.934
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667 1778 23 35106.089
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722 1938 23 34622.067
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725 1958 23 34719.411
726 1959 23 34889.110
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1005 803 44 14051.245
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1038 868 44 15750.000
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1080 949 44 16242.306
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1082 951 44 16753.731
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1085 954 44 18201.992
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1121 1016 44 18929.144
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1224 1267 44 22699.119
1225 1272 44 25334.019
1226 1273 44 26024.027
1227 1277 44 29201.884
1228 1278 44 30084.257
1229 1283 44 22134.532
1230 1284 44 22134.532
1231 1285 44 22210.639
1232 1286 44 22362.077
1233 1289 44 23250.000
1234 1293 44 25334.019
1235 1294 44 25991.585
1236 1295 44 26696.208
1237 1298 44 29057.056
1238 1299 44 29915.506
1239 1304 44 22500.000
1240 1305 44 22537.469
1241 1307 44 22835.006
1242 1308 44 23092.206
1243 1310 44 23811.762
1244 1316 44 27372.431
1245 1317 44 28132.499
1246 1318 44 28930.952
1247 1319 44 29764.702
1248 1320 44 30630.867
1249 1324 44 22957.842
1250 1325 44 22884.219
1251 1326 44 22884.219
1252 1327 44 22957.842
1253 1328 44 23104.383
1254 1329 44 23322.468
1255 1330 44 23610.114
1256 1331 44 23964.818
1257 1333 44 24863.377
1258 1334 44 25400.541
1259 1335 44 25991.585
1260 1336 44 26632.921
1261 1337 44 27321.008
1262 1338 44 28052.406
1263 1339 44 28823.818
1264 1340 44 29632.119
1265 1341 44 30474.374
1266 1346 44 23250.000
1267 1347 44 23286.262
1268 1348 44 23394.711
1269 1349 44 23574.350
1270 1350 44 23823.570
1271 1353 44 24964.975
1272 1354 44 25466.890
1273 1355 44 26024.027
1274 1357 44 27290.108
1275 1358 44 27992.186
1276 1359 44 28735.866
1277 1360 44 29518.003
1278 1361 44 30335.623
1279 1362 44 31185.934
1280 1368 44 23633.927
1281 1369 44 23705.221
1282 1370 44 23847.170
1283 1371 44 24058.522
1284 1372 44 24337.471
1285 1373 44 24681.724
1286 1374 44 25088.593
1287 1375 44 25555.088
1288 1376 44 26078.008
1289 1378 44 27279.800
1290 1379 44 27951.968
1291 1380 44 28667.272
1292 1381 44 29422.568
1293 1382 44 30214.856
1294 1383 44 31041.303
1295 1388 44 24000.000
1296 1389 44 24035.131
1297 1390 44 24140.215
1298 1391 44 24314.348
1299 1392 44 24556.058
1300 1393 44 24863.377
1301 1394 44 25233.906
1302 1395 44 25664.908
1303 1396 44 26153.394
1304 1397 44 26696.208
1305 1398 44 27290.108
1306 1400 44 28618.176
1307 1401 44 29345.996
1308 1402 44 30112.290
1309 1404 44 31749.016
1310 1410 44 24383.652
1311 1411 44 24452.761
1312 1412 44 24590.394
1313 1413 44 24795.413
1314 1414 44 25066.162
1315 1415 44 25400.541
1316 1416 44 25796.075
1317 1417 44 26250.000
1318 1418 44 26759.344
1319 1419 44 27321.008
1320 1422 44 29288.436
1321 1423 44 30028.112
1322 1430 44 24750.000
1323 1431 44 24784.067
1324 1432 44 24885.990
1325 1433 44 25054.940
1326 1434 44 25289.573
1327 1435 44 25588.083
1328 1436 44 25948.266
1329 1437 44 26367.594
1330 1438 44 26843.295
1331 1439 44 27372.431
1332 1440 44 27951.968
1333 1442 44 29250.000
1334 1443 44 29962.477
1335 1444 44 30713.393
1336 1452 44 25133.394
1337 1453 44 25200.446
1338 1454 44 25334.019
1339 1455 44 25533.067
1340 1456 44 25796.075
1341 1457 44 26121.112
1342 1458 44 26505.896
1343 1459 44 26947.866
1344 1460 44 27444.262
1345 1461 44 27992.186
1346 1464 44 29915.506
1347 1465 44 30640.047
1348 1472 44 25500.000
1349 1473 44 25533.067
1350 1474 44 25632.011
1351 1475 44 25796.075
1352 1476 44 26024.027
1353 1477 44 26314.207
1354 1478 44 26664.583
1355 1479 44 27072.818
1356 1480 44 27536.340
1357 1481 44 28052.406
1358 1482 44 28618.176
1359 1485 44 30584.923
1360 1494 44 25883.151
1361 1495 44 25948.266
1362 1496 44 26078.008
1363 1497 44 26271.420
1364 1498 44 26527.109
1365 1499 44 26843.295
1366 1500 44 27217.871
1367 1501 44 27648.463
1368 1502 44 28132.499
1369 1506 44 30548.118
1370 1514 44 26250.000
1371 1515 44 26282.123
1372 1516 44 26378.258
1373 1517 44 26537.709
1374 1518 44 26759.344
1375 1519 44 27041.635
1376 1520 44 27382.704
1377 1521 44 27780.389
1378 1522 44 28232.295
1379 1523 44 28735.866
1380 1524 44 29288.436
1381 1527 44 31212.978
1382 1535 44 26632.921
1383 1536 44 26632.921
1384 1537 44 26696.208
1385 1538 44 26822.332
1386 1539 44 27010.415
1387 1540 44 27259.173
1388 1541 44 27566.964
1389 1542 44 27931.837
1390 1543 44 28351.587
1391 1544 44 28823.818
1392 1547 44 30529.699
1393 1556 44 27000.000
1394 1557 44 27031.232
1395 1558 44 27124.712
1396 1559 44 27279.800
1397 1560 44 27495.454
1398 1561 44 27770.263
1399 1562 44 28102.491
1400 1563 44 28490.130
1401 1564 44 28930.952
1402 1565 44 29422.568
1403 1566 44 29962.477
1404 1567 44 30548.118
1405 1568 44 31176.915
1406 1578 44 27382.704
1407 1580 44 27566.964
1408 1581 44 27750.000
1409 1582 44 27992.186
1410 1583 44 28292.004
1411 1584 44 28647.644
1412 1585 44 29057.056
1413 1586 44 29518.003
1414 1589 44 31185.934
1415 1590 44 31828.643
1416 1600 44 27871.356
1417 1601 44 28022.313
1418 1602 44 28232.295
1419 1603 44 28500.000
1420 1604 44 28823.818
1421 1605 44 29201.884
1422 1606 44 29632.119
1423 1607 44 30112.290
1424 1608 44 30640.047
1425 1609 44 31212.978
1426 1610 44 31828.643
1427 1622 44 28311.879
1428 1623 44 28490.130
1429 1624 44 28726.077
1430 1625 44 29018.313
1431 1626 44 29365.158
1432 1627 44 29764.702
1433 1628 44 30214.856
1434 1629 44 30713.393
1435 1630 44 31257.999
1436 1631 44 31846.311
1437 1642 44 28618.176
1438 1643 44 28765.213
1439 1644 44 28969.812
1440 1645 44 29230.763
1441 1646 44 29546.573
1442 1647 44 29915.506
1443 1648 44 30335.623
1444 1649 44 30804.829
1445 1650 44 31320.920
1446 1664 44 29057.056
1447 1665 44 29230.763
1448 1666 44 29460.779
1449 1667 44 29745.798
1450 1668 44 30084.257
1451 1670 44 30914.196
1452 1671 44 31401.632
1453 1672 44 31934.503
1454 1684 44 29365.158
1455 1685 44 29508.473
1456 1686 44 29707.953
1457 1687 44 29962.477
1458 1688 44 30270.654
1459 1689 44 30630.867
1460 1690 44 31041.303
1461 1691 44 31500.000
1462 1692 44 32004.882
1463 1706 44 29802.475
1464 1707 44 29971.862
1465 1708 44 30196.233
1466 1709 44 30474.374
1467 1710 44 30804.829
1468 1711 44 31185.934
1469 1712 44 31615.858
1470 1713 44 32092.639
1471 1714 44 32614.222
1472 1726 44 30112.290
1473 1727 44 30252.066
1474 1728 44 30446.675
1475 1729 44 30695.073
1476 1730 44 30995.967
1477 1731 44 31347.847
1478 1732 44 31749.016
1479 1733 44 32197.632
1480 1734 44 32691.742
1481 1735 44 33229.317
1482 1748 44 30548.118
1483 1749 44 30713.393
1484 1750 44 30932.386
1485 1751 44 31203.966
1486 1753 44 31899.255
1487 1754 44 32319.692
1488 1755 44 32786.239
1489 1756 44 33296.959
1490 1757 44 33849.852
1491 1768 44 30859.561
1492 1769 44 30995.967
1493 1770 44 31185.934
1494 1771 44 31428.490
1495 1772 44 31722.429
1496 1774 44 32458.628
1497 1775 44 32897.568
1498 1776 44 33381.320
1499 1777 44 33907.964
1500 1778 44 34475.535
1501 1790 44 31293.969
1502 1791 44 31455.325
1503 1792 44 31669.188
1504 1793 44 31934.503
1505 1794 44 32250.000
1506 1795 44 32614.222
1507 1796 44 33025.558
1508 1797 44 33482.271
1509 1798 44 33982.532
1510 1799 44 34524.448
1511 1800 44 35106.089
1512 1811 44 31740.156
1513 1812 44 31925.695
1514 1813 44 32162.672
1515 1817 44 33599.665
1516 1818 44 34073.450
1517 1819 44 34589.558
1518 1820 44 35146.124
1519 1832 44 32040.014
1520 1833 44 32197.632
1521 1834 44 32406.597
1522 1835 44 32665.923
1523 1837 44 33330.729
1524 1839 44 34180.587
1525 1840 44 34670.773
1526 1841 44 35202.095
1527 1842 44 35772.720
1528 1853 44 32484.612
1529 1854 44 32665.923
1530 1855 44 32897.568
1531 1858 44 33883.071
1532 1860 44 34767.981
1533 1861 44 35273.928
1534 1862 44 35819.862
1535 1874 44 32786.239
1536 1875 44 32940.287
1537 1876 44 33144.570
1538 1877 44 33398.166
1539 1880 44 34442.888
1540 1882 44 35361.526
1541 1883 44 35882.621
1542 1895 44 33229.317
1543 1896 44 33406.586
1544 1897 44 33633.131
1545 1898 44 33907.964
1546 1901 44 35009.820
1547 1903 44 35960.916
1548 1916 44 33532.633
1549 1917 44 33683.267
1550 1918 44 33883.071
1551 1919 44 34131.181
1552 1922 44 35154.125
1553 1924 44 36054.646
1554 1937 44 33974.255
1555 1938 44 34147.657
1556 1939 44 34369.318
1557 1942 44 35313.772
1558 1958 44 34279.185
1559 1959 44 34426.552
1560 1960 44 34622.067
1561 1961 44 34864.918
1562 1962 44 35154.125
1563 1964 44 35866.942
1564 1979 44 34719.411
1565 1980 44 34889.110
1566 1981 44 35106.089
1567 1982 44 35369.478
1568 1983 44 35678.250
1569 1984 44 36031.236
1570 1985 44 36427.153
1571 2001 44 35170.122
1572 2002 44 35361.526
1573 2003 44 35599.333
1574 2004 44 35882.621
1575 2005 44 36210.323
1576 2006 44 36581.245
1577 2007 44 36994.087
1578 2022 44 35630.921
1579 2023 44 35843.409
1580 2024 44 36101.420
1581 2025 44 36403.983
1582 2026 44 36750.000
1583 2027 44 37138.255
1584 2043 44 35913.960
1585 2044 44 36101.420
1586 2045 44 36334.385
1587 2046 44 36611.986
1588 2047 44 36933.217
1589 2048 44 37296.950
1590 2049 44 37701.956
1591 2064 44 36373.067
1592 2065 44 36581.245
1593 2066 44 36834.087
1594 2067 44 37130.681
1595 2068 44 37469.988
1596 2069 44 37850.859
1597 2085 44 36658.048
1598 2086 44 36841.722
1599 2087 44 37070.035
1600 2088 44 37342.168
1601 2089 44 37657.171
1602 2090 44 38013.978
1603 2091 44 38411.424
1604 2105 44 36956.055
1605 2106 44 37115.529
1606 2107 44 37319.566
1607 2108 44 37567.439
1608 2109 44 37858.288
1609 2110 44 38191.131
1610 2111 44 38564.880
1611 2128 44 37582.409
1612 2129 44 37806.249
1613 2130 44 38073.120
1614 2131 44 38382.125
1615 2132 44 38732.254
1616 2148 44 37858.288
1617 2149 44 38058.343
1618 2150 44 38301.436
1619 2151 44 38586.753
1620 2152 44 38913.365
1621 2153 44 39280.243
1622 2171 44 38542.995
1623 2172 44 38804.800
1624 2173 44 39108.023
1625 2174 44 39451.711
1626 2191 44 38797.551
1627 2192 44 39036.041
1628 2193 44 39316.027
1629 2194 44 39636.631
1630 2195 44 39996.875
1631 2212 44 39064.850
1632 2213 44 39280.243
1633 2214 44 39537.166
1634 2215 44 39834.815
1635 2216 44 40172.285
1636 2233 44 39537.166
1637 2234 44 39771.221
1638 2235 44 40046.067
1639 2236 44 40360.872
1640 2237 44 40714.709
1641 2256 44 40270.181
1642 2257 44 40562.452
1643 2258 44 40893.918
1644 2259 44 41263.634
1645 2277 44 40776.832
1646 2278 44 41086.038
1647 2279 44 41433.682
1648 2280 44 41818.806
1649 2297 44 40756.135
1650 2300 44 41616.553
1651 2301 44 41979.906
1652 2318 44 41243.181
1653 2319 44 41508.282
1654 2322 44 42532.341
1655 2337 44 41127.090
1656 2341 44 42020.084
1657 2343 44 42697.336
1658 2361 44 42240.384
1659 2364 44 43247.110
1660 2379 44 41872.575
1661 2384 44 43064.632
1662 2405 44 43596.875
1663 2406 44 43963.053
1664 2426 44 43789.982
1665 2427 44 44135.445
1666 2505 44 44109.948
1667 23 46 3269.174
1668 44 46 2598.076
1669 66 46 1984.313
1670 67 46 750.000
1671 68 46 750.000
1672 86 46 2704.163
1673 87 46 1500.000
1674 88 46 750.000
1675 89 46 1500.000
1676 90 46 2704.163
1677 109 46 1299.038
1678 110 46 1299.038
1679 111 46 2250.000
1680 112 46 3436.932
1681 128 46 3000.000
1682 129 46 1984.313
1683 130 46 1500.000
1684 131 46 1984.313
1685 132 46 3000.000
1686 133 46 4175.823
1687 134 46 5408.327
1688 150 46 2704.163
1689 151 46 1984.313
1690 152 46 1984.313
1691 153 46 2704.163
1692 154 46 3750.000
1693 155 46 4918.079
1694 156 46 6139.015
1695 172 46 2250.000
1696 174 46 3436.932
1697 175 46 4500.000
1698 176 46 5662.376
1699 195 46 3269.174
1700 196 46 4175.823
1701 197 46 5250.000
1702 215 46 3269.174
1703 216 46 3968.627
1704 217 46 4918.079
1705 237 46 3897.114
1706 238 46 4683.748
1707 239 46 5662.376
1708 257 46 3968.627
1709 258 46 4562.072
1710 259 46 5408.327
1711 278 46 4175.823
1712 279 46 4562.072
1713 280 46 5250.000
1714 298 46 4500.000
1715 299 46 4683.748
1716 300 46 5196.152
1717 320 46 4918.079
1718 321 46 5250.000
1719 322 46 5857.687
1720 340 46 5250.000
1721 341 46 5408.327
1722 342 46 5857.687
1723 363 46 5952.940
1724 364 46 6495.191
1725 383 46 6139.015
1726 384 46 6538.348
1727 385 46 7154.544
1728 404 46 6408.003
1729 405 46 6666.146
1730 406 46 7154.544
1731 407 46 7830.230
1732 408 46 8649.422
1733 425 46 6873.864
1734 426 46 7232.738
1735 427 46 7794.229
1736 428 46 8518.363
1737 429 46 9367.497
1738 446 46 7154.544
1739 447 46 7386.643
1740 448 46 7830.230
1741 449 46 8452.071
1742 450 46 9216.154
1743 452 46 11048.190
1744 468 46 7937.254
1745 469 46 8452.071
1746 470 46 9124.144
1747 471 46 9921.567
1748 472 46 10816.654
1749 488 46 7901.740
1750 489 46 8112.490
1751 490 46 8518.363
1752 491 46 9093.267
1753 493 46 10633.085
1754 494 46 11546.103
1755 509 46 8351.647
1756 510 46 8649.422
1757 511 46 9124.144
1758 512 46 9750.000
1759 513 46 10500.000
1760 514 46 11349.559
1761 515 46 12278.029
1762 530 46 8649.422
1763 531 46 8842.370
1764 532 46 9216.154
1765 533 46 9750.000
1766 534 46 10419.333
1767 536 46 12070.108
1768 537 46 13012.014
1769 538 46 14011.156
1770 539 46 15056.145
1771 551 46 9093.267
1772 552 46 9367.497
1773 553 46 9807.523
1774 554 46 10392.305
1775 555 46 11098.986
1776 559 46 14754.237
1777 560 46 15803.481
1778 561 46 16887.495
1779 572 46 9397.473
1780 573 46 9575.359
1781 574 46 9921.567
1782 575 46 10419.333
1783 576 46 11048.190
1784 578 46 12616.953
1785 582 46 16551.057
1786 583 46 17636.964
1787 584 46 18750.000
1788 593 46 9836.158
1789 594 46 10090.218
1790 595 46 10500.000
1791 596 46 11048.190
1792 597 46 11715.375
1793 603 46 17298.844
1794 604 46 18386.476
1795 615 46 10310.795
1796 616 46 10633.085
1797 617 46 11098.986
1798 618 46 11691.343
1799 620 46 13183.797
1800 637 46 11199.888
1801 638 46 11715.375
1802 657 46 11048.190
1803 658 46 11349.559
1804 659 46 11787.175
1805 660 46 12346.558
1806 662 46 13768.170
1807 677 46 11324.752
1808 678 46 11546.103
1809 679 46 11905.881
1810 680 46 12392.034
1811 681 46 12990.381
1812 699 46 11787.175
1813 700 46 12070.108
1814 701 46 12482.488
1815 702 46 13012.014
1816 704 46 14367.933
1817 722 46 13076.697
1818 723 46 13645.054
1819 725 46 15056.145
1820 730 46 19672.316
1821 741 46 12527.470
1822 743 46 13183.797
1823 744 46 13686.216
1824 752 46 20415.986
1825 761 46 12816.006
1826 762 46 13012.014
1827 763 46 13332.292
1828 764 46 13768.170
1829 765 46 14309.088
1830 767 46 15660.460
1831 772 46 20138.582
1832 784 46 13520.817
1833 785 46 13890.194
1834 786 46 14367.933
1835 787 46 14943.644
1836 788 46 15606.489
1837 789 46 16345.871
1838 803 46 13562.356
1839 804 46 13747.727
1840 805 46 14051.245
1841 806 46 14465.476
1842 807 46 14981.238
1843 808 46 15588.457
1844 809 46 16276.901
1845 810 46 17036.725
1846 814 46 20621.591
1847 819 46 25883.151
1848 824 46 13890.194
1849 826 46 14250.000
1850 827 46 14600.942
1851 828 46 15056.145
1852 829 46 15606.489
1853 830 46 16242.306
1854 831 46 16953.982
1855 832 46 17732.386
1856 835 46 20388.416
1857 844 46 14250.000
1858 846 46 14484.906
1859 847 46 14773.287
1860 848 46 15167.811
1861 849 46 15660.460
1862 850 46 16242.306
1863 851 46 16904.142
1864 852 46 17636.964
1865 853 46 18432.309
1866 854 46 19282.440
1867 855 46 20180.436
1868 856 46 21120.192
1869 866 46 14639.416
1870 867 46 14754.237
1871 868 46 14981.238
1872 869 46 15315.433
1873 870 46 15750.000
1874 871 46 16276.901
1875 872 46 16887.495
1876 873 46 17573.062
1877 874 46 18325.188
1878 875 46 19136.026
1879 876 46 19998.437
1880 877 46 20906.040
1881 887 46 15056.145
1882 888 46 15223.337
1883 889 46 15497.984
1884 890 46 15874.508
1885 891 46 16345.871
1886 893 46 17541.023
1887 894 46 18248.288
1888 895 46 19018.083
1889 896 46 19843.135
1890 897 46 20716.841
1891 898 46 21633.308
1892 908 46 15388.713
1893 909 46 15497.984
1894 910 46 15714.245
1895 911 46 16033.169
1896 912 46 16448.784
1897 915 46 18201.992
1898 917 46 19715.159
1899 918 46 20553.284
1900 919 46 21437.409
1901 928 46 15750.000
1902 929 46 15803.481
1903 930 46 15962.847
1904 931 46 16224.981
1905 932 46 16585.008
1906 933 46 17036.725
1907 934 46 17573.062
1908 935 46 18186.533
1909 938 46 20415.986
1910 939 46 21266.170
1911 940 46 22159.930
1912 942 46 24058.522
1913 949 46 16138.076
1914 950 46 16138.076
1915 951 46 16242.306
1916 952 46 16448.784
1917 953 46 16753.731
1918 954 46 17151.895
1919 955 46 17636.964
1920 956 46 18201.992
1921 957 46 18839.785
1922 959 46 20305.480
1923 961 46 21981.526
1924 962 46 22884.219
1925 970 46 16500.000
1926 971 46 16551.057
1927 972 46 16703.293
1928 973 46 16953.982
1929 974 46 17298.844
1930 975 46 17732.386
1931 976 46 18248.288
1932 977 46 18839.785
1933 978 46 19500.000
1934 982 46 22699.119
1935 983 46 23610.114
1936 984 46 24556.058
1937 991 46 16887.495
1938 992 46 16887.495
1939 993 46 16987.127
1940 994 46 17184.659
1941 995 46 17476.770
1942 996 46 17858.821
1943 997 46 18325.188
1944 998 46 18869.618
1945 999 46 19485.572
1946 1001 46 20906.040
1947 1003 46 22537.469
1948 1004 46 23418.742
1949 1011 46 17298.844
1950 1012 46 17250.000
1951 1013 46 17298.844
1952 1014 46 17444.555
1953 1015 46 17684.739
1954 1016 46 18015.618
1955 1018 46 18929.144
1956 1019 46 19500.000
1957 1023 46 22399.777
1958 1024 46 23250.000
1959 1025 46 24140.215
1960 1033 46 17636.964
1961 1034 46 17636.964
1962 1035 46 17732.386
1963 1036 46 17921.705
1964 1037 46 18201.992
1965 1043 46 21515.982
1966 1045 46 23104.383
1967 1046 46 23964.818
1968 1053 46 18046.814
1969 1054 46 18000.000
1970 1055 46 18046.814
1971 1056 46 18186.533
1972 1064 46 22197.973
1973 1066 46 23811.762
1974 1067 46 24681.724
1975 1074 46 18478.027
1976 1075 46 18386.476
1977 1076 46 18386.476
1978 1077 46 18478.027
1979 1079 46 18929.144
1980 1085 46 22134.532
1981 1087 46 23681.480
1982 1088 46 24521.674
1983 1094 46 18929.144
1984 1095 46 18794.946
1985 1096 46 18750.000
1986 1097 46 18794.946
1987 1098 46 18929.144
1988 1099 46 19150.718
1989 1105 46 22096.380
1990 1106 46 22810.359
1991 1107 46 23574.350
1992 1108 46 24383.652
1993 1109 46 25233.906
1994 1116 46 19224.008
1995 1117 46 19136.026
1996 1118 46 19136.026
1997 1119 46 19224.008
1998 1120 46 19398.776
1999 1129 46 24268.035
2000 1130 46 25088.593
2001 1131 46 25948.266
2002 1136 46 19672.316
2003 1138 46 19500.000
2004 1139 46 19543.221
2005 1141 46 19885.610
2006 1148 46 23430.749
2007 1149 46 24175.142
2008 1150 46 24964.975
2009 1151 46 25796.075
2010 1157 46 20138.582
2011 1158 46 19970.290
2012 1159 46 19885.610
2013 1160 46 19885.610
2014 1171 46 24863.377
2015 1172 46 25664.908
2016 1173 46 26505.896
2017 1174 46 27382.704
2018 1178 46 20415.986
2019 1179 46 20291.624
2020 1180 46 20250.000
2021 1181 46 20291.624
2022 1183 46 20621.591
2023 1190 46 24058.522
2024 1192 46 25555.088
2025 1193 46 26367.594
2026 1194 46 27217.871
2027 1199 46 20879.116
2028 1200 46 20716.841
2029 1201 46 20635.225
2030 1203 46 20716.841
2031 1204 46 20879.116
2032 1211 46 24035.131
2033 1214 46 26250.000
2034 1215 46 27072.818
2035 1216 46 27931.837
2036 1220 46 21160.104
2037 1221 46 21040.140
2038 1222 46 21000.000
2039 1223 46 21040.140
2040 1224 46 21160.104
2041 1231 46 24035.131
2042 1232 46 24693.116
2043 1235 46 26947.866
2044 1236 46 27780.389
2045 1241 46 21620.303
2046 1242 46 21463.632
2047 1243 46 21384.866
2048 1246 46 21620.303
2049 1256 46 26843.295
2050 1257 46 27648.463
2051 1258 46 28490.130
2052 1262 46 21904.623
2053 1263 46 21788.759
2054 1264 46 21750.000
2055 1265 46 21788.759
2056 1266 46 21904.623
2057 1267 46 22096.380
2058 1272 46 24105.238
2059 1273 46 24693.116
2060 1277 46 27536.340
2061 1278 46 28351.587
2062 1283 46 22362.077
2063 1284 46 22210.639
2064 1285 46 22134.532
2065 1286 46 22134.532
2066 1289 46 22587.331
2067 1293 46 24175.142
2068 1294 46 24727.262
2069 1295 46 25334.019
2070 1298 46 27444.262
2071 1299 46 28232.295
2072 1304 46 22649.503
2073 1305 46 22537.469
2074 1307 46 22537.469
2075 1308 46 22649.503
2076 1310 46 23092.206
2077 1316 46 25980.762
2078 1317 46 26654.033
2079 1318 46 27372.431
2080 1319 46 28132.499
2081 1320 46 28930.952
2082 1324 46 23322.468
2083 1325 46 23104.383
2084 1326 46 22957.842
2085 1327 46 22884.219
2086 1328 46 22884.219
2087 1329 46 22957.842
2088 1330 46 23104.383
2089 1331 46 23322.468
2090 1333 46 23964.818
2091 1334 46 24383.652
2092 1335 46 24863.377
2093 1336 46 25400.541
2094 1337 46 25991.585
2095 1338 46 26632.921
2096 1339 46 27321.008
2097 1340 46 28052.406
2098 1341 46 28823.818
2099 1346 46 23394.711
2100 1347 46 23286.262
2101 1348 46 23250.000
2102 1349 46 23286.262
2103 1350 46 23394.711
2104 1353 46 24140.215
2105 1354 46 24521.674
2106 1355 46 24964.975
2107 1357 46 26024.027
2108 1358 46 26632.921
2109 1359 46 27290.108
2110 1360 46 27992.186
2111 1361 46 28735.866
2112 1362 46 29518.003
2113 1368 46 23705.221
2114 1369 46 23633.927
2115 1370 46 23633.927
2116 1371 46 23705.221
2117 1372 46 23847.170
2118 1373 46 24058.522
2119 1374 46 24337.471
2120 1375 46 24681.724
2121 1376 46 25088.593
2122 1378 46 26078.008
2123 1379 46 26654.033
2124 1380 46 27279.800
2125 1381 46 27951.968
2126 1382 46 28667.272
2127 1383 46 29422.568
2128 1388 46 24140.215
2129 1389 46 24035.131
2130 1390 46 24000.000
2131 1391 46 24035.131
2132 1392 46 24140.215
2133 1393 46 24314.348
2134 1394 46 24556.058
2135 1395 46 24863.377
2136 1396 46 25233.906
2137 1397 46 25664.908
2138 1398 46 26153.394
2139 1400 46 27290.108
2140 1401 46 27931.837
2141 1402 46 28618.176
2142 1404 46 30112.290
2143 1410 46 24452.761
2144 1411 46 24383.652
2145 1412 46 24383.652
2146 1413 46 24452.761
2147 1414 46 24590.394
2148 1415 46 24795.413
2149 1416 46 25066.162
2150 1417 46 25400.541
2151 1418 46 25796.075
2152 1419 46 26250.000
2153 1422 46 27931.837
2154 1423 46 28588.678
2155 1430 46 24885.990
2156 1431 46 24784.067
2157 1432 46 24750.000
2158 1433 46 24784.067
2159 1434 46 24885.990
2160 1435 46 25054.940
2161 1436 46 25289.573
2162 1437 46 25588.083
2163 1438 46 25948.266
2164 1439 46 26367.594
2165 1440 46 26843.295
2166 1442 46 27951.968
2167 1443 46 28578.838
2168 1444 46 29250.000
2169 1452 46 25200.446
2170 1453 46 25133.394
2171 1454 46 25133.394
2172 1455 46 25200.446
2173 1456 46 25334.019
2174 1457 46 25533.067
2175 1458 46 25796.075
2176 1459 46 26121.112
2177 1460 46 26505.896
2178 1461 46 26947.866
2179 1464 46 28588.678
2180 1465 46 29230.763
2181 1472 46 25632.011
2182 1473 46 25533.067
2183 1474 46 25500.000
2184 1475 46 25533.067
2185 1476 46 25632.011
2186 1477 46 25796.075
2187 1478 46 26024.027
2188 1479 46 26314.207
2189 1480 46 26664.583
2190 1481 46 27072.818
2191 1482 46 27536.340
2192 1485 46 29230.763
2193 1494 46 25948.266
2194 1495 46 25883.151
2195 1496 46 25883.151
2196 1497 46 25948.266
2197 1498 46 26078.008
2198 1499 46 26271.420
2199 1500 46 26527.109
2200 1501 46 26843.295
2201 1502 46 27217.871
2202 1506 46 29250.000
2203 1514 46 26378.258
2204 1515 46 26282.123
2205 1516 46 26250.000
2206 1517 46 26282.123
2207 1518 46 26378.258
2208 1519 46 26537.709
2209 1520 46 26759.344
2210 1521 46 27041.635
2211 1522 46 27382.704
2212 1523 46 27780.389
2213 1524 46 28232.295
2214 1527 46 29887.288
2215 1535 46 26822.332
2216 1536 46 26696.208
2217 1537 46 26632.921
2218 1538 46 26632.921
2219 1539 46 26696.208
2220 1540 46 26822.332
2221 1541 46 27010.415
2222 1542 46 27259.173
2223 1543 46 27566.964
2224 1544 46 27931.837
2225 1547 46 29345.996
2226 1556 46 27124.712
2227 1557 46 27031.232
2228 1558 46 27000.000
2229 1559 46 27031.232
2230 1560 46 27124.712
2231 1561 46 27279.800
2232 1562 46 27495.454
2233 1563 46 27770.263
2234 1564 46 28102.491
2235 1565 46 28490.130
2236 1566 46 28930.952
2237 1567 46 29422.568
2238 1568 46 29962.477
2239 1578 46 27444.262
2240 1580 46 27382.704
2241 1581 46 27444.262
2242 1582 46 27566.964
2243 1583 46 27750.000
2244 1584 46 27992.186
2245 1585 46 28292.004
2246 1586 46 28647.644
2247 1589 46 30028.112
2248 1590 46 30584.923
2249 1600 46 27750.000
2250 1601 46 27780.389
2251 1602 46 27871.356
2252 1603 46 28022.313
2253 1604 46 28232.295
2254 1605 46 28500.000
2255 1606 46 28823.818
2256 1607 46 29201.884
2257 1608 46 29632.119
2258 1609 46 30112.290
2259 1610 46 30640.047
2260 1622 46 28132.499
2261 1623 46 28192.419
2262 1624 46 28311.879
2263 1625 46 28490.130
2264 1626 46 28726.077
2265 1627 46 29018.313
2266 1628 46 29365.158
2267 1629 46 29764.702
2268 1630 46 30214.856
2269 1631 46 30713.393
2270 1642 46 28500.000
2271 1643 46 28529.590
2272 1644 46 28618.176
2273 1645 46 28765.213
2274 1646 46 28969.812
2275 1647 46 29230.763
2276 1648 46 29546.573
2277 1649 46 29915.506
2278 1650 46 30335.623
2279 1664 46 28882.304
2280 1665 46 28940.672
2281 1666 46 29057.056
2282 1667 46 29230.763
2283 1668 46 29460.779
2284 1670 46 30084.257
2285 1671 46 30474.374
2286 1672 46 30914.196
2287 1684 46 29250.000
2288 1685 46 29278.832
2289 1686 46 29365.158
2290 1687 46 29508.473
2291 1688 46 29707.953
2292 1689 46 29962.477
2293 1690 46 30270.654
2294 1691 46 30630.867
2295 1692 46 31041.303
2296 1706 46 29632.119
2297 1707 46 29689.013
2298 1708 46 29802.475
2299 1709 46 29971.862
2300 1710 46 30196.233
2301 1711 46 30474.374
2302 1712 46 30804.829
2303 1713 46 31185.934
2304 1714 46 31615.858
2305 1726 46 30000.000
2306 1727 46 30028.112
2307 1728 46 30112.290
2308 1729 46 30252.066
2309 1730 46 30446.675
2310 1731 46 30695.073
2311 1732 46 30995.967
2312 1733 46 31347.847
2313 1734 46 31749.016
2314 1735 46 32197.632
2315 1748 46 30381.944
2316 1749 46 30437.436
2317 1750 46 30548.118
2318 1751 46 30713.393
2319 1753 46 31203.966
2320 1754 46 31526.774
2321 1755 46 31899.255
2322 1756 46 32319.692
2323 1757 46 32786.239
2324 1768 46 30750.000
2325 1769 46 30777.427
2326 1770 46 30859.561
2327 1771 46 30995.967
2328 1772 46 31185.934
2329 1774 46 31722.429
2330 1775 46 32066.337
2331 1776 46 32458.628
2332 1777 46 32897.568
2333 1778 46 33381.320
2334 1790 46 31131.776
2335 1791 46 31185.934
2336 1792 46 31293.969
2337 1793 46 31455.325
2338 1794 46 31669.188
2339 1795 46 31934.503
2340 1796 46 32250.000
2341 1797 46 32614.222
2342 1798 46 33025.558
2343 1799 46 33482.271
2344 1800 46 33982.532
2345 1811 46 31526.774
2346 1812 46 31606.961
2347 1813 46 31740.156
2348 1817 46 32786.239
2349 1818 46 33170.017
2350 1819 46 33599.665
2351 1820 46 34073.450
2352 1832 46 31881.617
2353 1833 46 31934.503
2354 1834 46 32040.014
2355 1835 46 32197.632
2356 1837 46 32665.923
2357 1839 46 33330.729
2358 1840 46 33733.329
2359 1841 46 34180.587
2360 1842 46 34670.773
2361 1853 46 32276.152
2362 1854 46 32354.482
2363 1855 46 32484.612
2364 1858 46 33178.495
2365 1860 46 33883.071
2366 1861 46 34303.790
2367 1862 46 34767.981
2368 1874 46 32631.465
2369 1875 46 32683.138
2370 1876 46 32786.239
2371 1877 46 32940.287
2372 1880 46 33699.963
2373 1882 46 34442.888
2374 1883 46 34881.048
2375 1895 46 33025.558
2376 1896 46 33102.115
2377 1897 46 33229.317
2378 1898 46 33406.586
2379 1901 46 34229.921
2380 1903 46 35009.820
2381 1916 46 33381.320
2382 1917 46 33431.834
2383 1918 46 33532.633
2384 1919 46 33683.267
2385 1922 46 34426.552
2386 1924 46 35154.125
2387 1937 46 33774.991
2388 1938 46 33849.852
2389 1939 46 33974.255
2390 1942 46 34638.310
2391 1958 46 34131.181
2392 1959 46 34180.587
2393 1960 46 34279.185
2394 1961 46 34426.552
2395 1962 46 34622.067
2396 1964 46 35154.125
2397 1979 46 34524.448
2398 1980 46 34597.688
2399 1981 46 34719.411
2400 1982 46 34889.110
2401 1983 46 35106.089
2402 1984 46 35369.478
2403 1985 46 35678.250
2404 2001 46 34929.393
2405 2002 46 35025.883
2406 2003 46 35170.122
2407 2004 46 35361.526
2408 2005 46 35599.333
2409 2006 46 35882.621
2410 2007 46 36210.323
2411 2022 46 35345.615
2412 2023 46 35464.771
2413 2024 46 35630.921
2414 2025 46 35843.409
2415 2026 46 36101.420
2416 2027 46 36403.983
2417 2043 46 35678.250
2418 2044 46 35772.720
2419 2045 46 35913.960
2420 2046 46 36101.420
2421 2047 46 36334.385
2422 2048 46 36611.986
2423 2049 46 36933.217
2424 2064 46 36093.628
2425 2065 46 36210.323
2426 2066 46 36373.067
2427 2067 46 36581.245
2428 2068 46 36834.087
2429 2069 46 37130.681
2430 2085 46 36427.153
2431 2086 46 36519.686
2432 2087 46 36658.048
2433 2088 46 36841.722
2434 2089 46 37070.035
2435 2090 46 37342.168
2436 2091 46 37657.171
2437 2105 46 36772.952
2438 2106 46 36841.722
2439 2107 46 36956.055
2440 2108 46 37115.529
2441 2109 46 37319.566
2442 2110 46 37567.439
2443 2111 46 37858.288
2444 2128 46 37266.775
2445 2129 46 37402.373
2446 2130 46 37582.409
2447 2131 46 37806.249
2448 2132 46 38073.120
2449 2148 46 37589.892
2450 2149 46 37701.956
2451 2150 46 37858.288
2452 2151 46 38058.343
2453 2152 46 38301.436
2454 2153 46 38586.753
2455 2171 46 38146.920
2456 2172 46 38323.459
2457 2173 46 38542.995
2458 2174 46 38804.800
2459 2191 46 38448.017
2460 2192 46 38601.328
2461 2193 46 38797.551
2462 2194 46 39036.041
2463 2195 46 39316.027
2464 2212 46 38761.289
2465 2213 46 38891.676
2466 2214 46 39064.850
2467 2215 46 39280.243
2468 2216 46 39537.166
2469 2233 46 39194.228
2470 2234 46 39344.631
2471 2235 46 39537.166
2472 2236 46 39771.221
2473 2237 46 40046.067
2474 2256 46 39806.564
2475 2257 46 40017.965
2476 2258 46 40270.181
2477 2259 46 40562.452
2478 2277 46 40277.165
2479 2278 46 40506.944
2480 2279 46 40776.832
2481 2280 46 41086.038
2482 2297 46 40381.772
2483 2300 46 41003.811
2484 2301 46 41290.889
2485 2318 46 40831.973
2486 2319 46 41017.527
2487 2322 46 41812.080
2488 2337 46 40921.419
2489 2341 46 41494.729
2490 2343 46 42020.084
2491 2361 46 41758.233
2492 2364 46 42538.953
2493 2379 46 41670.583
2494 2384 46 42472.785
2495 2405 46 42973.102
2496 2406 46 43266.615
2497 2426 46 43208.072
2498 2427 46 43480.599
2499 2505 46 43918.248
2500 23 66 1500.000
2501 44 66 750.000
2502 46 66 1984.313
2503 67 66 1299.038
2504 68 66 2598.076
2505 86 66 750.000
2506 87 66 750.000
2507 88 66 1984.313
2508 89 66 3269.174
2509 90 66 4562.072
2510 109 66 1500.000
2511 110 66 2704.163
2512 111 66 3968.627
2513 112 66 5250.000
2514 128 66 1299.038
2515 129 66 1299.038
2516 130 66 2250.000
2517 131 66 3436.932
2518 132 66 4683.748
2519 133 66 5952.940
2520 134 66 7232.738
2521 150 66 1500.000
2522 151 66 1984.313
2523 152 66 3000.000
2524 153 66 4175.823
2525 154 66 5408.327
2526 155 66 6666.146
2527 156 66 7937.254
2528 172 66 2704.163
2529 174 66 4918.079
2530 175 66 6139.015
2531 176 66 7386.643
2532 195 66 4500.000
2533 196 66 5662.376
2534 197 66 6873.864
2535 215 66 4175.823
2536 216 66 5250.000
2537 217 66 6408.003
2538 237 66 4918.079
2539 238 66 6000.000
2540 239 66 7154.544
2541 257 66 4683.748
2542 258 66 5662.376
2543 259 66 6750.000
2544 278 66 4562.072
2545 279 66 5408.327
2546 280 66 6408.003
2547 298 66 4562.072
2548 299 66 5250.000
2549 300 66 6139.015
2550 320 66 5196.152
2551 321 66 5952.940
2552 322 66 6873.864
2553 340 66 5250.000
2554 341 66 5857.687
2555 342 66 6666.146
2556 363 66 6538.348
2557 364 66 7386.643
2558 383 66 6495.191
2559 384 66 7232.738
2560 385 66 8112.490
2561 404 66 6538.348
2562 405 66 7154.544
2563 406 66 7937.254
2564 407 66 8842.370
2565 408 66 9836.158
2566 425 66 7154.544
2567 426 66 7830.230
2568 427 66 8649.422
2569 428 66 9575.359
2570 429 66 10580.052
2571 446 66 7232.738
2572 447 66 7794.229
2573 448 66 8518.363
2574 449 66 9367.497
2575 450 66 10310.795
2576 452 66 12392.034
2577 468 66 8452.071
2578 469 66 9216.154
2579 470 66 10090.218
2580 471 66 11048.190
2581 472 66 12070.108
2582 488 66 7937.254
2583 489 66 8452.071
2584 490 66 9124.144
2585 491 66 9921.567
2586 493 66 11787.175
2587 494 66 12816.006
2588 509 66 8518.363
2589 510 66 9093.267
2590 511 66 9807.523
2591 512 66 10633.085
2592 513 66 11546.103
2593 514 66 12527.470
2594 515 66 13562.356
2595 530 66 8649.422
2596 531 66 9124.144
2597 532 66 9750.000
2598 533 66 10500.000
2599 534 66 11349.559
2600 536 66 13268.855
2601 537 66 14309.088
2602 538 66 15388.713
2603 539 66 16500.000
2604 551 66 9216.154
2605 552 66 9750.000
2606 553 66 10419.333
2607 554 66 11199.888
2608 555 66 12070.108
2609 559 66 16138.076
2610 560 66 17250.000
2611 561 66 18386.476
2612 572 66 9367.497
2613 573 66 9807.523
2614 574 66 10392.305
2615 575 66 11098.986
2616 576 66 11905.881
2617 578 66 13747.727
2618 582 66 18000.000
2619 583 66 19136.026
2620 584 66 20291.624
2621 593 66 9921.567
2622 594 66 10419.333
2623 595 66 11048.190
2624 596 66 11787.175
2625 597 66 12616.953
2626 603 66 18750.000
2627 604 66 19885.610
2628 615 66 10500.000
2629 616 66 11048.190
2630 617 66 11715.375
2631 618 66 12482.488
2632 620 66 14250.000
2633 637 66 11691.343
2634 638 66 12392.034
2635 657 66 11199.888
2636 658 66 11715.375
2637 659 66 12346.558
2638 660 66 13076.697
2639 662 66 14773.287
2640 677 66 11349.559
2641 678 66 11787.175
2642 679 66 12346.558
2643 680 66 13012.014
2644 681 66 13768.170
2645 699 66 11905.881
2646 700 66 12392.034
2647 701 66 12990.381
2648 702 66 13686.216
2649 704 66 15315.433
2650 722 66 13645.054
2651 723 66 14367.933
2652 725 66 16033.169
2653 730 66 21040.140
2654 741 66 12616.953
2655 743 66 13645.054
2656 744 66 14309.088
2657 752 66 21788.759
2658 761 66 12794.042
2659 762 66 13183.797
2660 763 66 13686.216
2661 764 66 14289.419
2662 765 66 14981.238
2663 767 66 16585.008
2664 772 66 21463.632
2665 784 66 13768.170
2666 785 66 14309.088
2667 786 66 14943.644
2668 787 66 15660.460
2669 788 66 16448.784
2670 789 66 17298.844
2671 803 66 13520.817
2672 804 66 13890.194
2673 805 66 14367.933
2674 806 66 14943.644
2675 807 66 15606.489
2676 808 66 16345.871
2677 809 66 17151.895
2678 810 66 18015.618
2679 814 66 21904.623
2680 819 66 27382.704
2681 824 66 13747.727
2682 826 66 14465.476
2683 827 66 14981.238
2684 828 66 15588.457
2685 829 66 16276.901
2686 830 66 17036.725
2687 831 66 17858.821
2688 832 66 18734.994
2689 835 66 21620.303
2690 844 66 14011.156
2691 846 66 14600.942
2692 847 66 15056.145
2693 848 66 15606.489
2694 849 66 16242.306
2695 850 66 16953.982
2696 851 66 17732.386
2697 852 66 18569.128
2698 853 66 19456.683
2699 854 66 20388.416
2700 855 66 21358.546
2701 856 66 22362.077
2702 866 66 14484.906
2703 867 66 14773.287
2704 868 66 15167.811
2705 869 66 15660.460
2706 870 66 16242.306
2707 871 66 16904.142
2708 872 66 17636.964
2709 873 66 18432.309
2710 874 66 19282.440
2711 875 66 20180.436
2712 876 66 21120.192
2713 877 66 22096.380
2714 887 66 14981.238
2715 888 66 15315.433
2716 889 66 15750.000
2717 890 66 16276.901
2718 891 66 16887.495
2719 893 66 18325.188
2720 894 66 19136.026
2721 895 66 19998.437
2722 896 66 20906.040
2723 897 66 21853.203
2724 898 66 22835.006
2725 908 66 15223.337
2726 909 66 15497.984
2727 910 66 15874.508
2728 911 66 16345.871
2729 912 66 16904.142
2730 915 66 19018.083
2731 917 66 20716.841
2732 918 66 21633.308
2733 919 66 22587.331
2734 928 66 15497.984
2735 929 66 15714.245
2736 930 66 16033.169
2737 931 66 16448.784
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9493 1118 109 18046.814
9494 1119 109 18186.533
9495 1120 109 18417.044
9496 1129 109 23811.762
9497 1130 109 24681.724
9498 1131 109 25588.083
9499 1136 109 18478.027
9500 1138 109 18386.476
9501 1139 109 18478.027
9502 1141 109 18929.144
9503 1148 109 22884.219
9504 1149 109 23681.480
9505 1150 109 24521.674
9506 1151 109 25400.541
9507 1157 109 18929.144
9508 1158 109 18794.946
9509 1159 109 18750.000
9510 1160 109 18794.946
9511 1171 109 24383.652
9512 1172 109 25233.906
9513 1173 109 26121.112
9514 1174 109 27041.635
9515 1178 109 19224.008
9516 1179 109 19136.026
9517 1180 109 19136.026
9518 1181 109 19224.008
9519 1183 109 19658.014
9520 1190 109 23490.690
9521 1192 109 25088.593
9522 1193 109 25948.266
9523 1194 109 26843.295
9524 1199 109 19672.316
9525 1200 109 19543.221
9526 1201 109 19500.000
9527 1203 109 19672.316
9528 1204 109 19885.610
9529 1211 109 23430.749
9530 1214 109 25796.075
9531 1215 109 26664.583
9532 1216 109 27566.964
9533 1220 109 19970.290
9534 1221 109 19885.610
9535 1222 109 19885.610
9536 1223 109 19970.290
9537 1224 109 20138.582
9538 1231 109 23394.711
9539 1232 109 24105.238
9540 1235 109 26505.896
9541 1236 109 27382.704
9542 1241 109 20415.986
9543 1242 109 20291.624
9544 1243 109 20250.000
9545 1246 109 20621.591
9546 1256 109 26367.594
9547 1257 109 27217.871
9548 1258 109 28102.491
9549 1262 109 20716.841
9550 1263 109 20635.225
9551 1264 109 20635.225
9552 1265 109 20716.841
9553 1266 109 20879.116
9554 1267 109 21120.192
9555 1272 109 23394.711
9556 1273 109 24035.131
9557 1277 109 27072.818
9558 1278 109 27931.837
9559 1283 109 21160.104
9560 1284 109 21040.140
9561 1285 109 21000.000
9562 1286 109 21040.140
9563 1289 109 21633.308
9564 1293 109 23430.749
9565 1294 109 24035.131
9566 1295 109 24693.116
9567 1298 109 26947.866
9568 1299 109 27780.389
9569 1304 109 21463.632
9570 1305 109 21384.866
9571 1307 109 21463.632
9572 1308 109 21620.303
9573 1310 109 22159.930
9574 1316 109 25356.212
9575 1317 109 26078.008
9576 1318 109 26843.295
9577 1319 109 27648.463
9578 1320 109 28490.130
9579 1324 109 22096.380
9580 1325 109 21904.623
9581 1326 109 21788.759
9582 1327 109 21750.000
9583 1328 109 21788.759
9584 1329 109 21904.623
9585 1330 109 22096.380
9586 1331 109 22362.077
9587 1333 109 23104.383
9588 1334 109 23574.350
9589 1335 109 24105.238
9590 1336 109 24693.116
9591 1337 109 25334.019
9592 1338 109 26024.027
9593 1339 109 26759.344
9594 1340 109 27536.340
9595 1341 109 28351.587
9596 1346 109 22210.639
9597 1347 109 22134.532
9598 1348 109 22134.532
9599 1349 109 22210.639
9600 1350 109 22362.077
9601 1353 109 23250.000
9602 1354 109 23681.480
9603 1355 109 24175.142
9604 1357 109 25334.019
9605 1358 109 25991.585
9606 1359 109 26696.208
9607 1360 109 27444.262
9608 1361 109 28232.295
9609 1362 109 29057.056
9610 1368 109 22537.469
9611 1369 109 22500.000
9612 1370 109 22537.469
9613 1371 109 22649.503
9614 1372 109 22835.006
9615 1373 109 23092.206
9616 1374 109 23418.742
9617 1375 109 23811.762
9618 1376 109 24268.035
9619 1378 109 25356.212
9620 1379 109 25980.762
9621 1380 109 26654.033
9622 1381 109 27372.431
9623 1382 109 28132.499
9624 1383 109 28930.952
9625 1388 109 22957.842
9626 1389 109 22884.219
9627 1390 109 22884.219
9628 1391 109 22957.842
9629 1392 109 23104.383
9630 1393 109 23322.468
9631 1394 109 23610.114
9632 1395 109 23964.818
9633 1396 109 24383.652
9634 1397 109 24863.377
9635 1398 109 25400.541
9636 1400 109 26632.921
9637 1401 109 27321.008
9638 1402 109 28052.406
9639 1404 109 29632.119
9640 1410 109 23286.262
9641 1411 109 23250.000
9642 1412 109 23286.262
9643 1413 109 23394.711
9644 1414 109 23574.350
9645 1415 109 23823.570
9646 1416 109 24140.215
9647 1417 109 24521.674
9648 1418 109 24964.975
9649 1419 109 25466.890
9650 1422 109 27290.108
9651 1423 109 27992.186
9652 1430 109 23705.221
9653 1431 109 23633.927
9654 1432 109 23633.927
9655 1433 109 23705.221
9656 1434 109 23847.170
9657 1435 109 24058.522
9658 1436 109 24337.471
9659 1437 109 24681.724
9660 1438 109 25088.593
9661 1439 109 25555.088
9662 1440 109 26078.008
9663 1442 109 27279.800
9664 1443 109 27951.968
9665 1444 109 28667.272
9666 1452 109 24035.131
9667 1453 109 24000.000
9668 1454 109 24035.131
9669 1455 109 24140.215
9670 1456 109 24314.348
9671 1457 109 24556.058
9672 1458 109 24863.377
9673 1459 109 25233.906
9674 1460 109 25664.908
9675 1461 109 26153.394
9676 1464 109 27931.837
9677 1465 109 28618.176
9678 1472 109 24452.761
9679 1473 109 24383.652
9680 1474 109 24383.652
9681 1475 109 24452.761
9682 1476 109 24590.394
9683 1477 109 24795.413
9684 1478 109 25066.162
9685 1479 109 25400.541
9686 1480 109 25796.075
9687 1481 109 26250.000
9688 1482 109 26759.344
9689 1485 109 28588.678
9690 1494 109 24784.067
9691 1495 109 24750.000
9692 1496 109 24784.067
9693 1497 109 24885.990
9694 1498 109 25054.940
9695 1499 109 25289.573
9696 1500 109 25588.083
9697 1501 109 25948.266
9698 1502 109 26367.594
9699 1506 109 28578.838
9700 1514 109 25200.446
9701 1515 109 25133.394
9702 1516 109 25133.394
9703 1517 109 25200.446
9704 1518 109 25334.019
9705 1519 109 25533.067
9706 1520 109 25796.075
9707 1521 109 26121.112
9708 1522 109 26505.896
9709 1523 109 26947.866
9710 1524 109 27444.262
9711 1527 109 29230.763
9712 1535 109 25632.011
9713 1536 109 25533.067
9714 1537 109 25500.000
9715 1538 109 25533.067
9716 1539 109 25632.011
9717 1540 109 25796.075
9718 1541 109 26024.027
9719 1542 109 26314.207
9720 1543 109 26664.583
9721 1544 109 27072.818
9722 1547 109 28618.176
9723 1556 109 25948.266
9724 1557 109 25883.151
9725 1558 109 25883.151
9726 1559 109 25948.266
9727 1560 109 26078.008
9728 1561 109 26271.420
9729 1562 109 26527.109
9730 1563 109 26843.295
9731 1564 109 27217.871
9732 1565 109 27648.463
9733 1566 109 28132.499
9734 1567 109 28667.272
9735 1568 109 29250.000
9736 1578 109 26282.123
9737 1580 109 26282.123
9738 1581 109 26378.258
9739 1582 109 26537.709
9740 1583 109 26759.344
9741 1584 109 27041.635
9742 1585 109 27382.704
9743 1586 109 27780.389
9744 1589 109 29288.436
9745 1590 109 29887.288
9746 1600 109 26632.921
9747 1601 109 26696.208
9748 1602 109 26822.332
9749 1603 109 27010.415
9750 1604 109 27259.173
9751 1605 109 27566.964
9752 1606 109 27931.837
9753 1607 109 28351.587
9754 1608 109 28823.818
9755 1609 109 29345.996
9756 1610 109 29915.506
9757 1622 109 27031.232
9758 1623 109 27124.712
9759 1624 109 27279.800
9760 1625 109 27495.454
9761 1626 109 27770.263
9762 1627 109 28102.491
9763 1628 109 28490.130
9764 1629 109 28930.952
9765 1630 109 29422.568
9766 1631 109 29962.477
9767 1642 109 27382.704
9768 1643 109 27444.262
9769 1644 109 27566.964
9770 1645 109 27750.000
9771 1646 109 27992.186
9772 1647 109 28292.004
9773 1648 109 28647.644
9774 1649 109 29057.056
9775 1650 109 29518.003
9776 1664 109 27780.389
9777 1665 109 27871.356
9778 1666 109 28022.313
9779 1667 109 28232.295
9780 1668 109 28500.000
9781 1670 109 29201.884
9782 1671 109 29632.119
9783 1672 109 30112.290
9784 1684 109 28132.499
9785 1685 109 28192.419
9786 1686 109 28311.879
9787 1687 109 28490.130
9788 1688 109 28726.077
9789 1689 109 29018.313
9790 1690 109 29365.158
9791 1691 109 29764.702
9792 1692 109 30214.856
9793 1706 109 28529.590
9794 1707 109 28618.176
9795 1708 109 28765.213
9796 1709 109 28969.812
9797 1710 109 29230.763
9798 1711 109 29546.573
9799 1712 109 29915.506
9800 1713 109 30335.623
9801 1714 109 30804.829
9802 1726 109 28882.304
9803 1727 109 28940.672
9804 1728 109 29057.056
9805 1729 109 29230.763
9806 1730 109 29460.779
9807 1731 109 29745.798
9808 1732 109 30084.257
9809 1733 109 30474.374
9810 1734 109 30914.196
9811 1735 109 31401.632
9812 1748 109 29278.832
9813 1749 109 29365.158
9814 1750 109 29508.473
9815 1751 109 29707.953
9816 1753 109 30270.654
9817 1754 109 30630.867
9818 1755 109 31041.303
9819 1756 109 31500.000
9820 1757 109 32004.882
9821 1768 109 29632.119
9822 1769 109 29689.013
9823 1770 109 29802.475
9824 1771 109 29971.862
9825 1772 109 30196.233
9826 1774 109 30804.829
9827 1775 109 31185.934
9828 1776 109 31615.858
9829 1777 109 32092.639
9830 1778 109 32614.222
9831 1790 109 30028.112
9832 1791 109 30112.290
9833 1792 109 30252.066
9834 1793 109 30446.675
9835 1794 109 30695.073
9836 1795 109 30995.967
9837 1796 109 31347.847
9838 1797 109 31749.016
9839 1798 109 32197.632
9840 1799 109 32691.742
9841 1800 109 33229.317
9842 1811 109 30437.436
9843 1812 109 30548.118
9844 1813 109 30713.393
9845 1817 109 31899.255
9846 1818 109 32319.692
9847 1819 109 32786.239
9848 1820 109 33296.959
9849 1832 109 30777.427
9850 1833 109 30859.561
9851 1834 109 30995.967
9852 1835 109 31185.934
9853 1837 109 31722.429
9854 1839 109 32458.628
9855 1840 109 32897.568
9856 1841 109 33381.320
9857 1842 109 33907.964
9858 1853 109 31185.934
9859 1854 109 31293.969
9860 1855 109 31455.325
9861 1858 109 32250.000
9862 1860 109 33025.558
9863 1861 109 33482.271
9864 1862 109 33982.532
9865 1874 109 31526.774
9866 1875 109 31606.961
9867 1876 109 31740.156
9868 1877 109 31925.695
9869 1880 109 32786.239
9870 1882 109 33599.665
9871 1883 109 34073.450
9872 1895 109 31934.503
9873 1896 109 32040.014
9874 1897 109 32197.632
9875 1898 109 32406.597
9876 1901 109 33330.729
9877 1903 109 34180.587
9878 1916 109 32276.152
9879 1917 109 32354.482
9880 1918 109 32484.612
9881 1919 109 32665.923
9882 1922 109 33507.462
9883 1924 109 34303.790
9884 1937 109 32683.138
9885 1938 109 32786.239
9886 1939 109 32940.287
9887 1942 109 33699.963
9888 1958 109 33025.558
9889 1959 109 33102.115
9890 1960 109 33229.317
9891 1961 109 33406.586
9892 1962 109 33633.131
9893 1964 109 34229.921
9894 1979 109 33431.834
9895 1980 109 33532.633
9896 1981 109 33683.267
9897 1982 109 33883.071
9898 1983 109 34131.181
9899 1984 109 34426.552
9900 1985 109 34767.981
9901 2001 109 33849.852
9902 2002 109 33974.255
9903 2003 109 34147.657
9904 2004 109 34369.318
9905 2005 109 34638.310
9906 2006 109 34953.541
9907 2007 109 35313.772
9908 2022 109 34279.185
9909 2023 109 34426.552
9910 2024 109 34622.067
9911 2025 109 34864.918
9912 2026 109 35154.125
9913 2027 109 35488.554
9914 2043 109 34597.688
9915 2044 109 34719.411
9916 2045 109 34889.110
9917 2046 109 35106.089
9918 2047 109 35369.478
9919 2048 109 35678.250
9920 2049 109 36031.236
9921 2064 109 35025.883
9922 2065 109 35170.122
9923 2066 109 35361.526
9924 2067 109 35599.333
9925 2068 109 35882.621
9926 2069 109 36210.323
9927 2085 109 35345.615
9928 2086 109 35464.771
9929 2087 109 35630.921
9930 2088 109 35843.409
9931 2089 109 36101.420
9932 2090 109 36403.983
9933 2091 109 36750.000
9934 2105 109 35678.250
9935 2106 109 35772.720
9936 2107 109 35913.960
9937 2108 109 36101.420
9938 2109 109 36334.385
9939 2110 109 36611.986
9940 2111 109 36933.217
9941 2128 109 36210.323
9942 2129 109 36373.067
9943 2130 109 36581.245
9944 2131 109 36834.087
9945 2132 109 37130.681
9946 2148 109 36519.686
9947 2149 109 36658.048
9948 2150 109 36841.722
9949 2151 109 37070.035
9950 2152 109 37342.168
9951 2153 109 37657.171
9952 2171 109 37115.529
9953 2172 109 37319.566
9954 2173 109 37567.439
9955 2174 109 37858.288
9956 2191 109 37402.373
9957 2192 109 37582.409
9958 2193 109 37806.249
9959 2194 109 38073.120
9960 2195 109 38382.125
9961 2212 109 37701.956
9962 2213 109 37858.288
9963 2214 109 38058.343
9964 2215 109 38301.436
9965 2216 109 38586.753
9966 2233 109 38146.920
9967 2234 109 38323.459
9968 2235 109 38542.995
9969 2236 109 38804.800
9970 2237 109 39108.023
9971 2256 109 38797.551
9972 2257 109 39036.041
9973 2258 109 39316.027
9974 2259 109 39636.631
9975 2277 109 39280.243
9976 2278 109 39537.166
9977 2279 109 39834.815
9978 2280 109 40172.285
9979 2297 109 39344.631
9980 2300 109 40046.067
9981 2301 109 40360.872
9982 2318 109 39806.564
9983 2319 109 40017.965
9984 2322 109 40893.918
9985 2337 109 39834.815
9986 2341 109 40506.944
9987 2343 109 41086.038
9988 2361 109 40756.135
9989 2364 109 41616.553
9990 2379 109 40583.248
9991 2384 109 41508.282
9992 2405 109 42020.084
9993 2406 109 42340.141
9994 2426 109 42240.384
9995 2427 109 42538.953
9996 2505 109 42828.875
9997 23 110 4175.823
9998 44 110 3436.932
9999 46 110 1299.038
10000 66 110 2704.163
10001 67 110 1500.000
10002 68 110 750.000
10003 86 110 3269.174
10004 87 110 1984.313
10005 88 110 750.000
10006 89 110 750.000
10007 90 110 1984.313
10008 109 110 1299.038
10009 111 110 1299.038
10010 112 110 2598.076
10011 128 110 3269.174
10012 129 110 1984.313
10013 130 110 750.000
10014 131 110 750.000
10015 132 110 1984.313
10016 133 110 3269.174
10017 134 110 4562.072
10018 150 110 2704.163
10019 151 110 1500.000
10020 152 110 750.000
10021 153 110 1500.000
10022 154 110 2704.163
10023 155 110 3968.627
10024 156 110 5250.000
10025 172 110 1299.038
10026 174 110 2250.000
10027 175 110 3436.932
10028 176 110 4683.748
10029 195 110 1984.313
10030 196 110 3000.000
10031 197 110 4175.823
10032 215 110 1984.313
10033 216 110 2704.163
10034 217 110 3750.000
10035 237 110 2598.076
10036 238 110 3436.932
10037 239 110 4500.000
10038 257 110 2704.163
10039 258 110 3269.174
10040 259 110 4175.823
10041 278 110 3000.000
10042 279 110 3269.174
10043 280 110 3968.627
10044 298 110 3436.932
10045 299 110 3436.932
10046 300 110 3897.114
10047 320 110 3750.000
10048 321 110 3968.627
10049 322 110 4562.072
10050 340 110 4175.823
10051 341 110 4175.823
10052 342 110 4562.072
10053 363 110 4683.748
10054 364 110 5196.152
10055 383 110 4918.079
10056 384 110 5250.000
10057 385 110 5857.687
10058 404 110 5250.000
10059 405 110 5408.327
10060 406 110 5857.687
10061 407 110 6538.348
10062 408 110 7386.643
10063 425 110 5662.376
10064 426 110 5952.940
10065 427 110 6495.191
10066 428 110 7232.738
10067 429 110 8112.490
10068 446 110 6000.000
10069 447 110 6139.015
10070 448 110 6538.348
10071 449 110 7154.544
10072 450 110 7937.254
10073 452 110 9836.158
10074 468 110 6666.146
10075 469 110 7154.544
10076 470 110 7830.230
10077 471 110 8649.422
10078 472 110 9575.359
10079 488 110 6750.000
10080 489 110 6873.864
10081 490 110 7232.738
10082 491 110 7794.229
10083 493 110 9367.497
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10085 509 110 7154.544
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10090 514 110 10090.218
10091 515 110 11048.190
10092 530 110 7500.000
10093 531 110 7611.669
10094 532 110 7937.254
10095 533 110 8452.071
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10099 538 110 12816.006
10100 539 110 13890.194
10101 551 110 7901.740
10102 552 110 8112.490
10103 553 110 8518.363
10104 554 110 9093.267
10105 555 110 9807.523
10106 559 110 13562.356
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10108 561 110 15750.000
10109 572 110 8250.000
10110 573 110 8351.647
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10115 582 110 15388.713
10116 583 110 16500.000
10117 584 110 17636.964
10118 593 110 8649.422
10119 594 110 8842.370
10120 595 110 9216.154
10121 596 110 9750.000
10122 597 110 10419.333
10123 603 110 16138.076
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10130 637 110 9921.567
10131 638 110 10419.333
10132 657 110 9836.158
10133 658 110 10090.218
10134 659 110 10500.000
10135 660 110 11048.190
10136 662 110 12482.488
10137 677 110 10145.812
10138 678 110 10310.795
10139 679 110 10633.085
10140 680 110 11098.986
10141 681 110 11691.343
10142 699 110 10580.052
10143 700 110 10816.654
10144 701 110 11199.888
10145 702 110 11715.375
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10148 723 110 12346.558
10149 725 110 13768.170
10150 730 110 18478.027
10151 741 110 11324.752
10152 743 110 11905.881
10153 744 110 12392.034
10154 752 110 19224.008
10155 761 110 11643.131
10156 762 110 11787.175
10157 763 110 12070.108
10158 764 110 12482.488
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10163 785 110 12616.953
10164 786 110 13076.697
10165 787 110 13645.054
10166 788 110 14309.088
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10168 803 110 12392.034
10169 804 110 12527.470
10170 805 110 12794.042
10171 806 110 13183.797
10172 807 110 13686.216
10173 808 110 14289.419
10174 809 110 14981.238
10175 810 110 15750.000
10176 814 110 19398.776
10177 819 110 24750.000
10178 824 110 12750.000
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10182 829 110 14309.088
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10185 832 110 16448.784
10186 835 110 19150.718
10187 844 110 13141.062
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10189 847 110 13520.817
10190 848 110 13890.194
10191 849 110 14367.933
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10194 852 110 16345.871
10195 853 110 17151.895
10196 854 110 18015.618
10197 855 110 18929.144
10198 856 110 19885.610
10199 866 110 13500.000
10200 867 110 13562.356
10201 868 110 13747.727
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10205 872 110 15588.457
10206 873 110 16276.901
10207 874 110 17036.725
10208 875 110 17858.821
10209 876 110 18734.994
10210 877 110 19658.014
10211 887 110 13890.194
10212 888 110 14011.156
10213 889 110 14250.000
10214 890 110 14600.942
10215 891 110 15056.145
10216 893 110 16242.306
10217 894 110 16953.982
10218 895 110 17732.386
10219 896 110 18569.128
10220 897 110 19456.683
10221 898 110 20388.416
10222 908 110 14250.000
10223 909 110 14309.088
10224 910 110 14484.906
10225 911 110 14773.287
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10230 919 110 20180.436
10231 928 110 14639.416
10232 929 110 14639.416
10233 930 110 14754.237
10234 931 110 14981.238
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10236 933 110 15750.000
10237 934 110 16276.901
10238 935 110 16887.495
10239 938 110 19136.026
10240 939 110 19998.437
10241 940 110 20906.040
10242 942 110 22835.006
10243 949 110 15056.145
10244 950 110 15000.000
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10364 1215 110 25796.075
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10369 1223 110 19885.610
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10379 1256 110 25555.088
10380 1257 110 26367.594
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10390 1277 110 26250.000
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10395 1286 110 21000.000
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10405 1308 110 21463.632
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10412 1324 110 22362.077
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10418 1330 110 21904.623
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10420 1333 110 22699.119
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10442 1362 110 28232.295
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10445 1370 110 22500.000
10446 1371 110 22537.469
10447 1372 110 22649.503
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10450 1375 110 23418.742
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10501 1454 110 24000.000
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13131 1712 128 30214.856
13132 1713 128 30713.393
13133 1714 128 31257.999
13134 1726 128 28618.176
13135 1727 128 28765.213
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13137 1729 128 29230.763
13138 1730 128 29546.573
13139 1731 128 29915.506
13140 1732 128 30335.623
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13142 1734 128 31320.920
13143 1735 128 31881.617
13144 1748 128 29057.056
13145 1749 128 29230.763
13146 1750 128 29460.779
13147 1751 128 29745.798
13148 1753 128 30474.374
13149 1754 128 30914.196
13150 1755 128 31401.632
13151 1756 128 31934.503
13152 1757 128 32510.575
13153 1768 128 29365.158
13154 1769 128 29508.473
13155 1770 128 29707.953
13156 1771 128 29962.477
13157 1772 128 30270.654
13158 1774 128 31041.303
13159 1775 128 31500.000
13160 1776 128 32004.882
13161 1777 128 32553.802
13162 1778 128 33144.570
13163 1790 128 29802.475
13164 1791 128 29971.862
13165 1792 128 30196.233
13166 1793 128 30474.374
13167 1794 128 30804.829
13168 1795 128 31185.934
13169 1796 128 31615.858
13170 1797 128 32092.639
13171 1798 128 32614.222
13172 1799 128 33178.495
13173 1800 128 33783.317
13174 1811 128 30252.066
13175 1812 128 30446.675
13176 1813 128 30695.073
13177 1817 128 32197.632
13178 1818 128 32691.742
13179 1819 128 33229.317
13180 1820 128 33808.283
13181 1832 128 30548.118
13182 1833 128 30713.393
13183 1834 128 30932.386
13184 1835 128 31203.966
13185 1837 128 31899.255
13186 1839 128 32786.239
13187 1840 128 33296.959
13188 1841 128 33849.852
13189 1842 128 34442.888
13190 1853 128 30995.967
13191 1854 128 31185.934
13192 1855 128 31428.490
13193 1858 128 32458.628
13194 1860 128 33381.320
13195 1861 128 33907.964
13196 1862 128 34475.535
13197 1874 128 31293.969
13198 1875 128 31455.325
13199 1876 128 31669.188
13200 1877 128 31934.503
13201 1880 128 33025.558
13202 1882 128 33982.532
13203 1883 128 34524.448
13204 1895 128 31740.156
13205 1896 128 31925.695
13206 1897 128 32162.672
13207 1898 128 32449.961
13208 1901 128 33599.665
13209 1903 128 34589.558
13210 1916 128 32040.014
13211 1917 128 32197.632
13212 1918 128 32406.597
13213 1919 128 32665.923
13214 1922 128 33733.329
13215 1924 128 34670.773
13216 1937 128 32484.612
13217 1938 128 32665.923
13218 1939 128 32897.568
13219 1942 128 33883.071
13220 1958 128 32786.239
13221 1959 128 32940.287
13222 1960 128 33144.570
13223 1961 128 33398.166
13224 1962 128 33699.963
13225 1964 128 34442.888
13226 1979 128 33229.317
13227 1980 128 33406.586
13228 1981 128 33633.131
13229 1982 128 33907.964
13230 1983 128 34229.921
13231 1984 128 34597.688
13232 1985 128 35009.820
13233 2001 128 33683.267
13234 2002 128 33883.071
13235 2003 128 34131.181
13236 2004 128 34426.552
13237 2005 128 34767.981
13238 2006 128 35154.125
13239 2007 128 35583.528
13240 2022 128 34147.657
13241 2023 128 34369.318
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16545 1919 132 31934.503
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19021 1853 150 30548.118
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19034 1883 150 33907.964
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19045 1922 150 33170.017
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19199 238 151 4175.823
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19210 320 151 3269.174
19211 321 151 3968.627
19212 322 151 4918.079
19213 340 151 3436.932
19214 341 151 3897.114
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19224 407 151 6873.864
19225 408 151 7901.740
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19230 429 151 8649.422
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19232 447 151 5857.687
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19245 491 151 7937.254
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19249 510 151 7154.544
19250 511 151 7830.230
19251 512 151 8649.422
19252 513 151 9575.359
19253 514 151 10580.052
19254 515 151 11643.131
19255 530 151 6873.864
19256 531 151 7232.738
19257 532 151 7794.229
19258 533 151 8518.363
19259 534 151 9367.497
19260 536 151 11324.752
19261 537 151 12392.034
19262 538 151 13500.000
19263 539 151 14639.416
19264 551 151 7386.643
19265 552 151 7830.230
19266 553 151 8452.071
19267 554 151 9216.154
19268 555 151 10090.218
19269 559 151 14250.000
19270 560 151 15388.713
19271 561 151 16551.057
19272 572 151 7611.669
19273 573 151 7937.254
19274 574 151 8452.071
19275 575 151 9124.144
19276 576 151 9921.567
19277 578 151 11787.175
19278 582 151 16138.076
19279 583 151 17298.844
19280 584 151 18478.027
19281 593 151 8112.490
19282 594 151 8518.363
19283 595 151 9093.267
19284 596 151 9807.523
19285 597 151 10633.085
19286 603 151 16887.495
19287 604 151 18046.814
19288 615 151 8649.422
19289 616 151 9124.144
19290 617 151 9750.000
19291 618 151 10500.000
19292 620 151 12278.029
19293 637 151 9750.000
19294 638 151 10419.333
19295 657 151 9367.497
19296 658 151 9807.523
19297 659 151 10392.305
19298 660 151 11098.986
19299 662 151 12794.042
19300 677 151 9575.359
19301 678 151 9921.567
19302 679 151 10419.333
19303 680 151 11048.190
19304 681 151 11787.175
19305 699 151 10090.218
19306 700 151 10500.000
19307 701 151 11048.190
19308 702 151 11715.375
19309 704 151 13332.292
19310 722 151 11691.343
19311 723 151 12392.034
19312 725 151 14051.245
19313 730 151 19136.026
19314 741 151 10816.654
19315 743 151 11715.375
19316 744 151 12346.558
19317 752 151 19885.610
19318 761 151 11048.190
19319 762 151 11349.559
19320 763 151 11787.175
19321 764 151 12346.558
19322 765 151 13012.014
19323 767 151 14600.942
19324 772 151 19543.221
19325 784 151 11905.881
19326 785 151 12392.034
19327 786 151 12990.381
19328 787 151 13686.216
19329 788 151 14465.476
19330 789 151 15315.433
19331 803 151 11787.175
19332 804 151 12070.108
19333 805 151 12482.488
19334 806 151 13012.014
19335 807 151 13645.054
19336 808 151 14367.933
19337 809 151 15167.811
19338 810 151 16033.169
19339 814 151 19970.290
19340 819 151 25533.067
19341 824 151 12070.108
19342 826 151 12616.953
19343 827 151 13076.697
19344 828 151 13645.054
19345 829 151 14309.088
19346 830 151 15056.145
19347 831 151 15874.508
19348 832 151 16753.731
19349 835 151 19672.316
19350 844 151 12392.034
19351 846 151 12794.042
19352 847 151 13183.797
19353 848 151 13686.216
19354 849 151 14289.419
19355 850 151 14981.238
19356 851 151 15750.000
19357 852 151 16585.008
19358 853 151 17476.770
19359 854 151 18417.044
19360 855 151 19398.776
19361 856 151 20415.986
19362 866 151 12816.006
19363 867 151 13012.014
19364 868 151 13332.292
19365 869 151 13768.170
19366 870 151 14309.088
19367 871 151 14943.644
19368 872 151 15660.460
19369 873 151 16448.784
19370 874 151 17298.844
19371 875 151 18201.992
19372 876 151 19150.718
19373 877 151 20138.582
19374 887 151 13268.855
19375 888 151 13520.817
19376 889 151 13890.194
19377 890 151 14367.933
19378 891 151 14943.644
19379 893 151 16345.871
19380 894 151 17151.895
19381 895 151 18015.618
19382 896 151 18929.144
19383 897 151 19885.610
19384 898 151 20879.116
19385 908 151 13562.356
19386 909 151 13747.727
19387 910 151 14051.245
19388 911 151 14465.476
19389 912 151 14981.238
19390 915 151 17036.725
19391 917 151 18734.994
19392 918 151 19658.014
19393 919 151 20621.591
19394 928 151 13890.194
19395 929 151 14011.156
19396 930 151 14250.000
19397 931 151 14600.942
19398 932 151 15056.145
19399 933 151 15606.489
19400 934 151 16242.306
19401 935 151 16953.982
19402 938 151 19456.683
19403 939 151 20388.416
19404 940 151 21358.546
19405 942 151 23394.711
19406 949 151 14250.000
19407 950 151 14309.088
19408 951 151 14484.906
19409 952 151 14773.287
19410 953 151 15167.811
19411 954 151 15660.460
19412 955 151 16242.306
19413 956 151 16904.142
19414 957 151 17636.964
19415 959 151 19282.440
19416 961 151 21120.192
19417 962 151 22096.380
19418 970 151 14639.416
19419 971 151 14754.237
19420 972 151 14981.238
19421 973 151 15315.433
19422 974 151 15750.000
19423 975 151 16276.901
19424 976 151 16887.495
19425 977 151 17573.062
19426 978 151 18325.188
19427 982 151 21853.203
19428 983 151 22835.006
19429 984 151 23847.170
19430 991 151 15000.000
19431 992 151 15056.145
19432 993 151 15223.337
19433 994 151 15497.984
19434 995 151 15874.508
19435 996 151 16345.871
19436 997 151 16904.142
19437 998 151 17541.023
19438 999 151 18248.288
19439 1001 151 19843.135
19440 1003 151 21633.308
19441 1004 151 22587.331
19442 1011 151 15388.713
19443 1012 151 15388.713
19444 1013 151 15497.984
19445 1014 151 15714.245
19446 1015 151 16033.169
19447 1016 151 16448.784
19448 1018 151 17541.023
19449 1019 151 18201.992
19450 1023 151 21437.409
19451 1024 151 22362.077
19452 1025 151 23322.468
19453 1033 151 15750.000
19454 1034 151 15803.481
19455 1035 151 15962.847
19456 1036 151 16224.981
19457 1037 151 16585.008
19458 1043 151 20415.986
19459 1045 151 22159.930
19460 1046 151 23092.206
19461 1053 151 16138.076
19462 1054 151 16138.076
19463 1055 151 16242.306
19464 1056 151 16448.784
19465 1064 151 21120.192
19466 1066 151 22884.219
19467 1067 151 23823.570
19468 1074 151 16551.057
19469 1075 151 16500.000
19470 1076 151 16551.057
19471 1077 151 16703.293
19472 1079 151 17298.844
19473 1085 151 21000.000
19474 1087 151 22699.119
19475 1088 151 23610.114
19476 1094 151 16987.127
19477 1095 151 16887.495
19478 1096 151 16887.495
19479 1097 151 16987.127
19480 1098 151 17184.659
19481 1099 151 17476.770
19482 1105 151 20906.040
19483 1106 151 21698.214
19484 1107 151 22537.469
19485 1108 151 23418.742
19486 1109 151 24337.471
19487 1116 151 17298.844
19488 1117 151 17250.000
19489 1118 151 17298.844
19490 1119 151 17444.555
19491 1120 151 17684.739
19492 1129 151 23250.000
19493 1130 151 24140.215
19494 1131 151 25066.162
19495 1136 151 17732.386
19496 1138 151 17636.964
19497 1139 151 17732.386
19498 1141 151 18201.992
19499 1148 151 22286.487
19500 1149 151 23104.383
19501 1150 151 23964.818
19502 1151 151 24863.377
19503 1157 151 18186.533
19504 1158 151 18046.814
19505 1159 151 18000.000
19506 1160 151 18046.814
19507 1171 151 23811.762
19508 1172 151 24681.724
19509 1173 151 25588.083
19510 1174 151 26527.109
19511 1178 151 18478.027
19512 1179 151 18386.476
19513 1180 151 18386.476
19514 1181 151 18478.027
19515 1183 151 18929.144
19516 1190 151 22884.219
19517 1192 151 24521.674
19518 1193 151 25400.541
19519 1194 151 26314.207
19520 1199 151 18929.144
19521 1200 151 18794.946
19522 1201 151 18750.000
19523 1203 151 18929.144
19524 1204 151 19150.718
19525 1211 151 22810.359
19526 1214 151 25233.906
19527 1215 151 26121.112
19528 1216 151 27041.635
19529 1220 151 19224.008
19530 1221 151 19136.026
19531 1222 151 19136.026
19532 1223 151 19224.008
19533 1224 151 19398.776
19534 1231 151 22760.986
19535 1232 151 23490.690
19536 1235 151 25948.266
19537 1236 151 26843.295
19538 1241 151 19672.316
19539 1242 151 19543.221
19540 1243 151 19500.000
19541 1246 151 19885.610
19542 1256 151 25796.075
19543 1257 151 26664.583
19544 1258 151 27566.964
19545 1262 151 19970.290
19546 1263 151 19885.610
19547 1264 151 19885.610
19548 1265 151 19970.290
19549 1266 151 20138.582
19550 1267 151 20388.416
19551 1272 151 22736.260
19552 1273 151 23394.711
19553 1277 151 26505.896
19554 1278 151 27382.704
19555 1283 151 20415.986
19556 1284 151 20291.624
19557 1285 151 20250.000
19558 1286 151 20291.624
19559 1289 151 20906.040
19560 1293 151 22760.986
19561 1294 151 23382.686
19562 1295 151 24058.522
19563 1298 151 26367.594
19564 1299 151 27217.871
19565 1304 151 20716.841
19566 1305 151 20635.225
19567 1307 151 20716.841
19568 1308 151 20879.116
19569 1310 151 21437.409
19570 1316 151 24727.262
19571 1317 151 25466.890
19572 1318 151 26250.000
19573 1319 151 27072.818
19574 1320 151 27931.837
19575 1324 151 21358.546
19576 1325 151 21160.104
19577 1326 151 21040.140
19578 1327 151 21000.000
19579 1328 151 21040.140
19580 1329 151 21160.104
19581 1330 151 21358.546
19582 1331 151 21633.308
19583 1333 151 22399.777
19584 1334 151 22884.219
19585 1335 151 23430.749
19586 1336 151 24035.131
19587 1337 151 24693.116
19588 1338 151 25400.541
19589 1339 151 26153.394
19590 1340 151 26947.866
19591 1341 151 27780.389
19592 1346 151 21463.632
19593 1347 151 21384.866
19594 1348 151 21384.866
19595 1349 151 21463.632
19596 1350 151 21620.303
19597 1353 151 22537.469
19598 1354 151 22982.330
19599 1355 151 23490.690
19600 1357 151 24681.724
19601 1358 151 25356.212
19602 1359 151 26078.008
19603 1360 151 26843.295
19604 1361 151 27648.463
19605 1362 151 28490.130
19606 1368 151 21788.759
19607 1369 151 21750.000
19608 1370 151 21788.759
19609 1371 151 21904.623
19610 1372 151 22096.380
19611 1373 151 22362.077
19612 1374 151 22699.119
19613 1375 151 23104.383
19614 1376 151 23574.350
19615 1378 151 24693.116
19616 1379 151 25334.019
19617 1380 151 26024.027
19618 1381 151 26759.344
19619 1382 151 27536.340
19620 1383 151 28351.587
19621 1388 151 22210.639
19622 1389 151 22134.532
19623 1390 151 22134.532
19624 1391 151 22210.639
19625 1392 151 22362.077
19626 1393 151 22587.331
19627 1394 151 22884.219
19628 1395 151 23250.000
19629 1396 151 23681.480
19630 1397 151 24175.142
19631 1398 151 24727.262
19632 1400 151 25991.585
19633 1401 151 26696.208
19634 1402 151 27444.262
19635 1404 151 29057.056
19636 1410 151 22537.469
19637 1411 151 22500.000
19638 1412 151 22537.469
19639 1413 151 22649.503
19640 1414 151 22835.006
19641 1415 151 23092.206
19642 1416 151 23418.742
19643 1417 151 23811.762
19644 1418 151 24268.035
19645 1419 151 24784.067
19646 1422 151 26654.033
19647 1423 151 27372.431
19648 1430 151 22957.842
19649 1431 151 22884.219
19650 1432 151 22884.219
19651 1433 151 22957.842
19652 1434 151 23104.383
19653 1435 151 23322.468
19654 1436 151 23610.114
19655 1437 151 23964.818
19656 1438 151 24383.652
19657 1439 151 24863.377
19658 1440 151 25400.541
19659 1442 151 26632.921
19660 1443 151 27321.008
19661 1444 151 28052.406
19662 1452 151 23286.262
19663 1453 151 23250.000
19664 1454 151 23286.262
19665 1455 151 23394.711
19666 1456 151 23574.350
19667 1457 151 23823.570
19668 1458 151 24140.215
19669 1459 151 24521.674
19670 1460 151 24964.975
19671 1461 151 25466.890
19672 1464 151 27290.108
19673 1465 151 27992.186
19674 1472 151 23705.221
19675 1473 151 23633.927
19676 1474 151 23633.927
19677 1475 151 23705.221
19678 1476 151 23847.170
19679 1477 151 24058.522
19680 1478 151 24337.471
19681 1479 151 24681.724
19682 1480 151 25088.593
19683 1481 151 25555.088
19684 1482 151 26078.008
19685 1485 151 27951.968
19686 1494 151 24035.131
19687 1495 151 24000.000
19688 1496 151 24035.131
19689 1497 151 24140.215
19690 1498 151 24314.348
19691 1499 151 24556.058
19692 1500 151 24863.377
19693 1501 151 25233.906
19694 1502 151 25664.908
19695 1506 151 27931.837
19696 1514 151 24452.761
19697 1515 151 24383.652
19698 1516 151 24383.652
19699 1517 151 24452.761
19700 1518 151 24590.394
19701 1519 151 24795.413
19702 1520 151 25066.162
19703 1521 151 25400.541
19704 1522 151 25796.075
19705 1523 151 26250.000
19706 1524 151 26759.344
19707 1527 151 28588.678
19708 1535 151 24885.990
19709 1536 151 24784.067
19710 1537 151 24750.000
19711 1538 151 24784.067
19712 1539 151 24885.990
19713 1540 151 25054.940
19714 1541 151 25289.573
19715 1542 151 25588.083
19716 1543 151 25948.266
19717 1544 151 26367.594
19718 1547 151 27951.968
19719 1556 151 25200.446
19720 1557 151 25133.394
19721 1558 151 25133.394
19722 1559 151 25200.446
19723 1560 151 25334.019
19724 1561 151 25533.067
19725 1562 151 25796.075
19726 1563 151 26121.112
19727 1564 151 26505.896
19728 1565 151 26947.866
19729 1566 151 27444.262
19730 1567 151 27992.186
19731 1568 151 28588.678
19732 1578 151 25533.067
19733 1580 151 25533.067
19734 1581 151 25632.011
19735 1582 151 25796.075
19736 1583 151 26024.027
19737 1584 151 26314.207
19738 1585 151 26664.583
19739 1586 151 27072.818
19740 1589 151 28618.176
19741 1590 151 29230.763
19742 1600 151 25883.151
19743 1601 151 25948.266
19744 1602 151 26078.008
19745 1603 151 26271.420
19746 1604 151 26527.109
19747 1605 151 26843.295
19748 1606 151 27217.871
19749 1607 151 27648.463
19750 1608 151 28132.499
19751 1609 151 28667.272
19752 1610 151 29250.000
19753 1622 151 26282.123
19754 1623 151 26378.258
19755 1624 151 26537.709
19756 1625 151 26759.344
19757 1626 151 27041.635
19758 1627 151 27382.704
19759 1628 151 27780.389
19760 1629 151 28232.295
19761 1630 151 28735.866
19762 1631 151 29288.436
19763 1642 151 26632.921
19764 1643 151 26696.208
19765 1644 151 26822.332
19766 1645 151 27010.415
19767 1646 151 27259.173
19768 1647 151 27566.964
19769 1648 151 27931.837
19770 1649 151 28351.587
19771 1650 151 28823.818
19772 1664 151 27031.232
19773 1665 151 27124.712
19774 1666 151 27279.800
19775 1667 151 27495.454
19776 1668 151 27770.263
19777 1670 151 28490.130
19778 1671 151 28930.952
19779 1672 151 29422.568
19780 1684 151 27382.704
19781 1685 151 27444.262
19782 1686 151 27566.964
19783 1687 151 27750.000
19784 1688 151 27992.186
19785 1689 151 28292.004
19786 1690 151 28647.644
19787 1691 151 29057.056
19788 1692 151 29518.003
19789 1706 151 27780.389
19790 1707 151 27871.356
19791 1708 151 28022.313
19792 1709 151 28232.295
19793 1710 151 28500.000
19794 1711 151 28823.818
19795 1712 151 29201.884
19796 1713 151 29632.119
19797 1714 151 30112.290
19798 1726 151 28132.499
19799 1727 151 28192.419
19800 1728 151 28311.879
19801 1729 151 28490.130
19802 1730 151 28726.077
19803 1731 151 29018.313
19804 1732 151 29365.158
19805 1733 151 29764.702
19806 1734 151 30214.856
19807 1735 151 30713.393
19808 1748 151 28529.590
19809 1749 151 28618.176
19810 1750 151 28765.213
19811 1751 151 28969.812
19812 1753 151 29546.573
19813 1754 151 29915.506
19814 1755 151 30335.623
19815 1756 151 30804.829
19816 1757 151 31320.920
19817 1768 151 28882.304
19818 1769 151 28940.672
19819 1770 151 29057.056
19820 1771 151 29230.763
19821 1772 151 29460.779
19822 1774 151 30084.257
19823 1775 151 30474.374
19824 1776 151 30914.196
19825 1777 151 31401.632
19826 1778 151 31934.503
19827 1790 151 29278.832
19828 1791 151 29365.158
19829 1792 151 29508.473
19830 1793 151 29707.953
19831 1794 151 29962.477
19832 1795 151 30270.654
19833 1796 151 30630.867
19834 1797 151 31041.303
19835 1798 151 31500.000
19836 1799 151 32004.882
19837 1800 151 32553.802
19838 1811 151 29689.013
19839 1812 151 29802.475
19840 1813 151 29971.862
19841 1817 151 31185.934
19842 1818 151 31615.858
19843 1819 151 32092.639
19844 1820 151 32614.222
19845 1832 151 30028.112
19846 1833 151 30112.290
19847 1834 151 30252.066
19848 1835 151 30446.675
19849 1837 151 30995.967
19850 1839 151 31749.016
19851 1840 151 32197.632
19852 1841 151 32691.742
19853 1842 151 33229.317
19854 1853 151 30437.436
19855 1854 151 30548.118
19856 1855 151 30713.393
19857 1858 151 31526.774
19858 1860 151 32319.692
19859 1861 151 32786.239
19860 1862 151 33296.959
19861 1874 151 30777.427
19862 1875 151 30859.561
19863 1876 151 30995.967
19864 1877 151 31185.934
19865 1880 151 32066.337
19866 1882 151 32897.568
19867 1883 151 33381.320
19868 1895 151 31185.934
19869 1896 151 31293.969
19870 1897 151 31455.325
19871 1898 151 31669.188
19872 1901 151 32614.222
19873 1903 151 33482.271
19874 1916 151 31526.774
19875 1917 151 31606.961
19876 1918 151 31740.156
19877 1919 151 31925.695
19878 1922 151 32786.239
19879 1924 151 33599.665
19880 1937 151 31934.503
19881 1938 151 32040.014
19882 1939 151 32197.632
19883 1942 151 32974.422
19884 1958 151 32276.152
19885 1959 151 32354.482
19886 1960 151 32484.612
19887 1961 151 32665.923
19888 1962 151 32897.568
19889 1964 151 33507.462
19890 1979 151 32683.138
19891 1980 151 32786.239
19892 1981 151 32940.287
19893 1982 151 33144.570
19894 1983 151 33398.166
19895 1984 151 33699.963
19896 1985 151 34048.678
19897 2001 151 33102.115
19898 2002 151 33229.317
19899 2003 151 33406.586
19900 2004 151 33633.131
19901 2005 151 33907.964
19902 2006 151 34229.921
19903 2007 151 34597.688
19904 2022 151 33532.633
19905 2023 151 33683.267
19906 2024 151 33883.071
19907 2025 151 34131.181
19908 2026 151 34426.552
19909 2027 151 34767.981
19910 2043 151 33849.852
19911 2044 151 33974.255
19912 2045 151 34147.657
19913 2046 151 34369.318
19914 2047 151 34638.310
19915 2048 151 34953.541
19916 2049 151 35313.772
19917 2064 151 34279.185
19918 2065 151 34426.552
19919 2066 151 34622.067
19920 2067 151 34864.918
19921 2068 151 35154.125
19922 2069 151 35488.554
19923 2085 151 34597.688
19924 2086 151 34719.411
19925 2087 151 34889.110
19926 2088 151 35106.089
19927 2089 151 35369.478
19928 2090 151 35678.250
19929 2091 151 36031.236
19930 2105 151 34929.393
19931 2106 151 35025.883
19932 2107 151 35170.122
19933 2108 151 35361.526
19934 2109 151 35599.333
19935 2110 151 35882.621
19936 2111 151 36210.323
19937 2128 151 35464.771
19938 2129 151 35630.921
19939 2130 151 35843.409
19940 2131 151 36101.420
19941 2132 151 36403.983
19942 2148 151 35772.720
19943 2149 151 35913.960
19944 2150 151 36101.420
19945 2151 151 36334.385
19946 2152 151 36611.986
19947 2153 151 36933.217
19948 2171 151 36373.067
19949 2172 151 36581.245
19950 2173 151 36834.087
19951 2174 151 37130.681
19952 2191 151 36658.048
19953 2192 151 36841.722
19954 2193 151 37070.035
19955 2194 151 37342.168
19956 2195 151 37657.171
19957 2212 151 36956.055
19958 2213 151 37115.529
19959 2214 151 37319.566
19960 2215 151 37567.439
19961 2216 151 37858.288
19962 2233 151 37402.373
19963 2234 151 37582.409
19964 2235 151 37806.249
19965 2236 151 38073.120
19966 2237 151 38382.125
19967 2256 151 38058.343
19968 2257 151 38301.436
19969 2258 151 38586.753
19970 2259 151 38913.365
19971 2277 151 38542.995
19972 2278 151 38804.800
19973 2279 151 39108.023
19974 2280 151 39451.711
19975 2297 151 38601.328
19976 2300 151 39316.027
19977 2301 151 39636.631
19978 2318 151 39064.850
19979 2319 151 39280.243
19980 2322 151 40172.285
19981 2337 151 39086.443
19982 2341 151 39771.221
19983 2343 151 40360.872
19984 2361 151 40017.965
19985 2364 151 40893.918
19986 2379 151 39834.815
19987 2384 151 40776.832
19988 2405 151 41290.889
19989 2406 151 41616.553
19990 2426 151 41508.282
19991 2427 151 41812.080
19992 2505 151 42080.280
19993 23 152 4500.000
19994 44 152 3750.000
19995 46 152 1984.313
19996 66 152 3000.000
19997 67 152 1984.313
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21501 1798 153 30630.867
21502 1799 153 31041.303
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21509 1819 153 31185.934
21510 1820 153 31615.858
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21513 1834 153 30028.112
21514 1835 153 30112.290
21515 1837 153 30446.675
21516 1839 153 30995.967
21517 1840 153 31347.847
21518 1841 153 31749.016
21519 1842 153 32197.632
21520 1853 153 30381.944
21521 1854 153 30381.944
21522 1855 153 30437.436
21523 1858 153 30932.386
21524 1860 153 31526.774
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21530 1877 153 30859.561
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21535 1896 153 31131.776
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21540 1916 153 31526.774
21541 1917 153 31500.000
21542 1918 153 31526.774
21543 1919 153 31606.961
21544 1922 153 32162.672
21545 1924 153 32786.239
21546 1937 153 31881.617
21547 1938 153 31881.617
21548 1939 153 31934.503
21549 1942 153 32406.597
21550 1958 153 32276.152
21551 1959 153 32250.000
21552 1960 153 32276.152
21553 1961 153 32354.482
21554 1962 153 32484.612
21555 1964 153 32897.568
21556 1979 153 32631.465
21557 1980 153 32631.465
21558 1981 153 32683.138
21559 1982 153 32786.239
21560 1983 153 32940.287
21561 1984 153 33144.570
21562 1985 153 33398.166
21563 2001 153 33000.000
21564 2002 153 33025.558
21565 2003 153 33102.115
21566 2004 153 33229.317
21567 2005 153 33406.586
21568 2006 153 33633.131
21569 2007 153 33907.964
21570 2022 153 33381.320
21571 2023 153 33431.834
21572 2024 153 33532.633
21573 2025 153 33683.267
21574 2026 153 33883.071
21575 2027 153 34131.181
21576 2043 153 33750.000
21577 2044 153 33774.991
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21579 2046 153 33974.255
21580 2047 153 34147.657
21581 2048 153 34369.318
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21589 2085 153 34500.000
21590 2086 153 34524.448
21591 2087 153 34597.688
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21594 2090 153 35106.089
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21600 2109 153 35170.122
21601 2110 153 35361.526
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21604 2129 153 35345.615
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21945 1015 154 15388.713
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21955 1036 154 15750.000
21956 1037 154 15803.481
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21965 1066 154 20305.480
21966 1067 154 21120.192
21967 1074 154 17298.844
21968 1075 154 16953.982
21969 1076 154 16703.293
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21971 1079 154 16551.057
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21976 1095 154 17476.770
21977 1096 154 17184.659
21978 1097 154 16987.127
21979 1098 154 16887.495
21980 1099 154 16887.495
21981 1105 154 18869.618
21982 1106 154 19485.572
21983 1107 154 20166.494
21984 1108 154 20906.040
21985 1109 154 21698.214
21986 1116 154 18015.618
21987 1117 154 17684.739
21988 1118 154 17444.555
21989 1119 154 17298.844
21990 1120 154 17250.000
21991 1129 154 20838.666
21992 1130 154 21594.270
21993 1131 154 22399.777
21994 1136 154 18569.128
21995 1138 154 17921.705
21996 1139 154 17732.386
21997 1141 154 17636.964
21998 1148 154 20138.582
21999 1149 154 20798.137
22000 1150 154 21515.982
22001 1151 154 22286.487
22002 1157 154 19136.026
22003 1158 154 18734.994
22004 1159 154 18417.044
22005 1160 154 18186.533
22006 1171 154 21463.632
22007 1172 154 22197.973
22008 1173 154 22982.330
22009 1174 154 23811.762
22010 1178 154 19282.440
22011 1179 154 18929.144
22012 1180 154 18659.783
22013 1181 154 18478.027
22014 1183 154 18386.476
22015 1190 154 20798.137
22016 1192 154 22134.532
22017 1193 154 22884.219
22018 1194 154 23681.480
22019 1199 154 19843.135
22020 1200 154 19456.683
22021 1201 154 19150.718
22022 1203 154 18794.946
22023 1204 154 18750.000
22024 1211 154 20838.666
22025 1214 154 22810.359
22026 1215 154 23574.350
22027 1216 154 24383.652
22028 1220 154 19998.437
22029 1221 154 19658.014
22030 1222 154 19398.776
22031 1223 154 19224.008
22032 1224 154 19136.026
22033 1231 154 20906.040
22034 1232 154 21463.632
22035 1235 154 23490.690
22036 1236 154 24268.035
22037 1241 154 20553.284
22038 1242 154 20180.436
22039 1243 154 19885.610
22040 1246 154 19500.000
22041 1256 154 23430.749
22042 1257 154 24175.142
22043 1258 154 24964.975
22044 1262 154 20716.841
22045 1263 154 20388.416
22046 1264 154 20138.582
22047 1265 154 19970.290
22048 1266 154 19885.610
22049 1267 154 19885.610
22050 1272 154 21120.192
22051 1273 154 21594.270
22052 1277 154 24105.238
22053 1278 154 24863.377
22054 1283 154 21266.170
22055 1284 154 20906.040
22056 1285 154 20621.591
22057 1286 154 20415.986
22058 1289 154 20291.624
22059 1293 154 21266.170
22060 1294 154 21698.214
22061 1295 154 22197.973
22062 1298 154 24058.522
22063 1299 154 24784.067
22064 1304 154 21437.409
22065 1305 154 21120.192
22066 1307 154 20716.841
22067 1308 154 20635.225
22068 1310 154 20716.841
22069 1316 154 22810.359
22070 1317 154 23394.711
22071 1318 154 24035.131
22072 1319 154 24727.262
22073 1320 154 25466.890
22074 1324 154 22399.777
22075 1325 154 21981.526
22076 1326 154 21633.308
22077 1327 154 21358.546
22078 1328 154 21160.104
22079 1329 154 21040.140
22080 1330 154 21000.000
22081 1331 154 21040.140
22082 1333 154 21358.546
22083 1334 154 21633.308
22084 1335 154 21981.526
22085 1336 154 22399.777
22086 1337 154 22884.219
22087 1338 154 23430.749
22088 1339 154 24035.131
22089 1340 154 24693.116
22090 1341 154 25400.541
22091 1346 154 22159.930
22092 1347 154 21853.203
22093 1348 154 21620.303
22094 1349 154 21463.632
22095 1350 154 21384.866
22096 1353 154 21620.303
22097 1354 154 21853.203
22098 1355 154 22159.930
22099 1357 154 22982.330
22100 1358 154 23490.690
22101 1359 154 24058.522
22102 1360 154 24681.724
22103 1361 154 25356.212
22104 1362 154 26078.008
22105 1368 154 22362.077
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22110 1373 154 21788.759
22111 1374 154 21904.623
22112 1375 154 22096.380
22113 1376 154 22362.077
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22120 1388 154 22884.219
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22122 1390 154 22362.077
22123 1391 154 22210.639
22124 1392 154 22134.532
22125 1393 154 22134.532
22126 1394 154 22210.639
22127 1395 154 22362.077
22128 1396 154 22587.331
22129 1397 154 22884.219
22130 1398 154 23250.000
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22132 1401 154 24727.262
22133 1402 154 25334.019
22134 1404 154 26696.208
22135 1410 154 23092.206
22136 1411 154 22835.006
22137 1412 154 22649.503
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22139 1414 154 22500.000
22140 1415 154 22537.469
22141 1416 154 22649.503
22142 1417 154 22835.006
22143 1418 154 23092.206
22144 1419 154 23418.742
22145 1422 154 24784.067
22146 1423 154 25356.212
22147 1430 154 23610.114
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22149 1432 154 23104.383
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22152 1435 154 22884.219
22153 1436 154 22957.842
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22156 1439 154 23610.114
22157 1440 154 23964.818
22158 1442 154 24863.377
22159 1443 154 25400.541
22160 1444 154 25991.585
22161 1452 154 23823.570
22162 1453 154 23574.350
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22164 1455 154 23286.262
22165 1456 154 23250.000
22166 1457 154 23286.262
22167 1458 154 23394.711
22168 1459 154 23574.350
22169 1460 154 23823.570
22170 1461 154 24140.215
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22172 1465 154 26024.027
22173 1472 154 24337.471
22174 1473 154 24058.522
22175 1474 154 23847.170
22176 1475 154 23705.221
22177 1476 154 23633.927
22178 1477 154 23633.927
22179 1478 154 23705.221
22180 1479 154 23847.170
22181 1480 154 24058.522
22182 1481 154 24337.471
22183 1482 154 24681.724
22184 1485 154 26078.008
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22186 1495 154 24314.348
22187 1496 154 24140.215
22188 1497 154 24035.131
22189 1498 154 24000.000
22190 1499 154 24035.131
22191 1500 154 24140.215
22192 1501 154 24314.348
22193 1502 154 24556.058
22194 1506 154 26153.394
22195 1514 154 25066.162
22196 1515 154 24795.413
22197 1516 154 24590.394
22198 1517 154 24452.761
22199 1518 154 24383.652
22200 1519 154 24383.652
22201 1520 154 24452.761
22202 1521 154 24590.394
22203 1522 154 24795.413
22204 1523 154 25066.162
22205 1524 154 25400.541
22206 1527 154 26759.344
22207 1535 154 25588.083
22208 1536 154 25289.573
22209 1537 154 25054.940
22210 1538 154 24885.990
22211 1539 154 24784.067
22212 1540 154 24750.000
22213 1541 154 24784.067
22214 1542 154 24885.990
22215 1543 154 25054.940
22216 1544 154 25289.573
22217 1547 154 26367.594
22218 1556 154 25796.075
22219 1557 154 25533.067
22220 1558 154 25334.019
22221 1559 154 25200.446
22222 1560 154 25133.394
22223 1561 154 25133.394
22224 1562 154 25200.446
22225 1563 154 25334.019
22226 1564 154 25533.067
22227 1565 154 25796.075
22228 1566 154 26121.112
22229 1567 154 26505.896
22230 1568 154 26947.866
22231 1578 154 26024.027
22232 1580 154 25632.011
22233 1581 154 25533.067
22234 1582 154 25500.000
22235 1583 154 25533.067
22236 1584 154 25632.011
22237 1585 154 25796.075
22238 1586 154 26024.027
22239 1589 154 27072.818
22240 1590 154 27536.340
22241 1600 154 26078.008
22242 1601 154 25948.266
22243 1602 154 25883.151
22244 1603 154 25883.151
22245 1604 154 25948.266
22246 1605 154 26078.008
22247 1606 154 26271.420
22248 1607 154 26527.109
22249 1608 154 26843.295
22250 1609 154 27217.871
22251 1610 154 27648.463
22252 1622 154 26378.258
22253 1623 154 26282.123
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22354 1854 154 30381.944
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22380 1938 154 31881.617
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22385 1960 154 32250.000
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22399 2004 154 33102.115
22400 2005 154 33229.317
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23210 1922 155 31740.156
23211 1924 155 32162.672
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23219 1961 155 32250.000
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23232 2004 155 33025.558
23233 2005 155 33102.115
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32783 1035 237 14250.000
32784 1036 237 14309.088
32785 1037 237 14484.906
32786 1043 237 17636.964
32787 1045 237 19282.440
32788 1046 237 20180.436
32789 1053 237 14981.238
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32792 1056 237 14639.416
32793 1064 237 18325.188
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32795 1067 237 20906.040
32796 1074 237 15497.984
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32799 1077 237 15000.000
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32801 1085 237 18248.288
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32810 1105 237 18201.992
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32815 1116 237 16224.981
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32817 1118 237 15803.481
32818 1119 237 15750.000
32819 1120 237 15803.481
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32829 1150 237 21120.192
32830 1151 237 21981.526
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32835 1171 237 21000.000
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32837 1173 237 22699.119
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32840 1179 237 17184.659
32841 1180 237 16987.127
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32843 1183 237 16987.127
32844 1190 237 20166.494
32845 1192 237 21698.214
32846 1193 237 22537.469
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32848 1199 237 18015.618
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32850 1201 237 17444.555
32851 1203 237 17250.000
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32855 1215 237 23250.000
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32861 1224 237 17636.964
32862 1231 237 20138.582
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32865 1236 237 23964.818
32866 1241 237 18734.994
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32870 1256 237 22982.330
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32877 1266 237 18386.476
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33137 1749 237 27000.000
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33139 1751 237 27124.712
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33183 1854 237 28882.304
33184 1855 237 28940.672
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33191 1876 237 29278.832
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33200 1901 237 30474.374
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33206 1922 237 30695.073
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33209 1938 237 30381.944
33210 1939 237 30437.436
33211 1942 237 30932.386
33212 1958 237 30777.427
33213 1959 237 30750.000
33214 1960 237 30777.427
33215 1961 237 30859.561
33216 1962 237 30995.967
33217 1964 237 31428.490
33218 1979 237 31131.776
33219 1980 237 31131.776
33220 1981 237 31185.934
33221 1982 237 31293.969
33222 1983 237 31455.325
33223 1984 237 31669.188
33224 1985 237 31934.503
33225 2001 237 31500.000
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33227 2003 237 31606.961
33228 2004 237 31740.156
33229 2005 237 31925.695
33230 2006 237 32162.672
33231 2007 237 32449.961
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33317 2406 237 39537.166
33318 2426 237 39537.166
33319 2427 237 39771.221
33320 2505 237 40500.000
33321 23 238 7500.000
33322 44 238 6750.000
33323 46 238 4683.748
33324 66 238 6000.000
33325 67 238 4918.079
33326 68 238 3968.627
33327 86 238 6408.003
33328 87 238 5250.000
33329 88 238 4175.823
33330 89 238 3269.174
33331 90 238 2704.163
33332 109 238 4500.000
33333 110 238 3436.932
[ reached 'max' / getOption("max.print") -- omitted 661389 rows ]Below code chunk is to rename the col headers to ORIGIN_GRID_ID and DESTIN_GRID_ID.
distPair <- distPair %>%
rename(ORIGIN_GRID_ID = Var1,
DESTIN_GRID_ID = Var2)Preparation of flow data
The flow data should contain the number of trips and distance for each origin/destination grid. In this section, we will proceed to create the desired dataframe.
flow_data <- od_data %>%
group_by(ORIGIN_GRID_ID, DESTIN_GRID_ID) %>%
summarize(TRIPS = sum(MORNING_PEAK)) `summarise()` has grouped output by 'ORIGIN_GRID_ID'. You can override using
the `.groups` argument.kable(head(flow_data, n = 10))| ORIGIN_GRID_ID | DESTIN_GRID_ID | TRIPS |
|---|---|---|
| 23 | 67 | 1 |
| 23 | 88 | 2 |
| 23 | 128 | 2 |
| 23 | 150 | 1 |
| 23 | 154 | 2 |
| 23 | 175 | 54 |
| 44 | 88 | 13 |
| 44 | 154 | 3 |
| 44 | 175 | 34 |
| 46 | 67 | 3 |
It is in our interest to create a column indicating intra-zonal pairs, ie distance should be 0.
flow_data$not_intra = ifelse(
flow_data$ORIGIN_GRID_ID == flow_data$DESTIN_GRID_ID,
0, flow_data$TRIPS)Now we will filter to keep only inter-zonal trips.
inter_zonal_flow = flow_data %>%
filter(not_intra >0)There are columns in inter_zonalflow and distPair tables that have columns of data types that need to changed into factor data type.
inter_zonal_flow$ORIGIN_GRID_ID <- as.factor(inter_zonal_flow$ORIGIN_GRID_ID)
inter_zonal_flow$DESTIN_GRID_ID <- as.factor(inter_zonal_flow$DESTIN_GRID_ID )
distPair$ORIGIN_GRID_ID <- as.factor(distPair$ORIGIN_GRID_ID)
distPair$DESTIN_GRID_ID <- as.factor(distPair$DESTIN_GRID_ID )We will now join both tables to create a table that contains distance and morning peak trips for each pair of grid_id.
flow_data1 <- inter_zonal_flow %>%
left_join (distPair,
by = c("ORIGIN_GRID_ID" = "ORIGIN_GRID_ID",
"DESTIN_GRID_ID" = "DESTIN_GRID_ID"))kable(head(flow_data1, n = 10))| ORIGIN_GRID_ID | DESTIN_GRID_ID | TRIPS | not_intra | dist |
|---|---|---|---|---|
| 23 | 67 | 1 | 1 | 2704.163 |
| 23 | 88 | 2 | 2 | 3436.932 |
| 23 | 128 | 2 | 2 | 1984.313 |
| 23 | 150 | 1 | 1 | 2598.076 |
| 23 | 154 | 2 | 2 | 6873.864 |
| 23 | 175 | 54 | 54 | 7611.669 |
| 44 | 88 | 13 | 13 | 2704.163 |
| 44 | 154 | 3 | 3 | 6139.015 |
| 44 | 175 | 34 | 34 | 6873.864 |
| 46 | 67 | 3 | 3 | 750.000 |
Display OD flows of Weekday morning peak
For od_data, we will remove intra-zonal trips like how we just did for flow_data.
od_data1 = od_data[od_data$ORIGIN_GRID_ID!=od_data$DESTIN_GRID_ID,]Below code chunk will create desire lines using od2line function.
flowline = od2line(flow = flow_data1,
zones = bs_count,
zone_code = 'grid_id')Creating centroids representing desire line start and end points.We will convert ORIGIN_GRID_ID and DESTIN_GRID_ID columns into factor data type,
od_data1$ORIGIN_GRID_ID <- as.factor(od_data1$ORIGIN_GRID_ID)
od_data1$DESTIN_GRID_ID <- as.factor(od_data1$DESTIN_GRID_ID)
flowline <- left_join(flowline, od_data1,
by = c('ORIGIN_GRID_ID' = 'ORIGIN_GRID_ID',
'DESTIN_GRID_ID' = 'DESTIN_GRID_ID')) %>%
select(-c(MORNING_PEAK))Visualization (flow map for trips more than 15k)
For first flow map, we will filter for trips more than 15kin order for us to analyze better. The length of line indicates the distance of each trip while the thickness indicates number of trips.
tmap_mode('view')tmap mode set to interactive viewingtmap_options(check.and.fix = TRUE)
filtered_flowline <- flowline %>%
filter(TRIPS >= 15000)
bs_count_filtered <- bs_count %>%
filter(grid_id %in% c(filtered_flowline$ORIGIN_GRID_ID, filtered_flowline$DESTIN_GRID_ID))
tm_shape(mpsz) +
tm_polygons(alpha=0.7,
col='grey') +
tm_shape(bs_count_filtered) +
tm_polygons(alpha=0.4) +
filtered_flowline %>%
tm_shape() +
tm_lines(lwd = 'TRIPS',
style = 'quantile',
scale= c(1, 3, 9, 15, 21, 30),
n = 6,
alpha= 0.5,
popup.vars=c("# Trips:"="TRIPS",
"Orig Desc:"="ORIGIN_DESC",
"Destin Desc:" = "DESTIN_DESC",
"Distance:" = 'dist'),
col='green') +
tm_view(set.zoom.limits = c(11,17)) +
tm_layout(main.title = 'O-D Flow On Weekday Morning Peak hour' ,
main.title.size = 1.0,
main.title.fontface = 'bold')Warning: The shape mpsz is invalid (after reprojection). See sf::st_is_validWarning in g$scale * (w_legend/maxW): longer object length is not a multiple of
shorter object lengthWarning in g$scale * (x/maxW): longer object length is not a multiple of
shorter object lengthLegend for line widths not available in view mode.Observations: We can see that the largest volume of flow is around Woodlands Causeway. If we click on some lines with high volumes, we can see that most of them end at schools and bus/MRT stations. This is expected as commuters are either going to institutions (for students) or transiting to another public transport (for working adults).
Visualization (flow map for trips more than 6k and distance more than 10km)
Now we vary the filter criteria and filter for trips with a considerable volume (6k) and distance in Singapore context (10km).
tmap_mode('view')tmap mode set to interactive viewingtmap_options(check.and.fix = TRUE)
filtered_flowline <- flowline %>%
filter(dist >= 10000,
TRIPS >= 6000)
bs_count_filtered <- bs_count %>%
filter(grid_id %in% c(filtered_flowline$ORIGIN_GRID_ID, filtered_flowline$DESTIN_GRID_ID))
tm_shape(mpsz) +
tm_polygons(alpha=0.7,
col='grey') +
tm_shape(bs_count_filtered) +
tm_polygons(alpha=0.4) +
filtered_flowline %>%
tm_shape() +
tm_lines(lwd = 'TRIPS',
style = 'quantile',
scale= c(1, 3, 9, 15, 21, 30),
n = 6,
alpha= 0.5,
popup.vars=c("# Trips:"="TRIPS",
"Orig Desc:"="ORIGIN_DESC",
"Destin Desc:" = "DESTIN_DESC",
"Distance:" = 'dist'),
col='green') +
tm_view(set.zoom.limits = c(11,17)) +
tm_layout(main.title = 'O-D Flow On Weekday Morning Peak hour' ,
main.title.size = 1.0,
main.title.fontface = 'bold')Warning: The shape mpsz is invalid (after reprojection). See sf::st_is_validWarning in g$scale * (w_legend/maxW): longer object length is not a multiple of
shorter object lengthWarning in g$scale * (x/maxW): longer object length is not a multiple of
shorter object lengthLegend for line widths not available in view mode.Observation: We can see that the trips are mostly from northern part to eastern part of Singapore. This could be due to the bus services that transport commuters from North to East via espressways, making it a convenient choice.
Creation of Origin / Destination Attributes
Importing attractiveness and propulsiveness data (geospatial)
We will now import our first attractiveness data, which is train station exit. This is because commuters normally connect to MRT stations and take the MRT to reach their destination.
train_exit = st_read(dsn='data/geospatial',
layer='Train_Station_Exit_Layer') %>%
st_transform(crs=3414)Reading layer `Train_Station_Exit_Layer' from data source
`C:\jjwoo\WOO JIA JIAN (clean by Prof Kam)\Take-home_Ex2\data\geospatial'
using driver `ESRI Shapefile'
Simple feature collection with 565 features and 2 fields
Geometry type: POINT
Dimension: XY
Bounding box: xmin: 6134.086 ymin: 27499.7 xmax: 45356.36 ymax: 47865.92
Projected CRS: SVY21Below code chunk will remove duplicated fields for stn_name and exit code columns.
train_exit <- train_exit %>%
distinct(stn_name,
exit_code,
.keep_all = TRUE)
glimpse(train_exit)Rows: 556
Columns: 3
$ stn_name <chr> "MACPHERSON MRT STATION", "MACPHERSON MRT STATION", "MACPHER…
$ exit_code <chr> "Exit A", "Exit B", "Exit C", "Exit B", "Exit A", "Exit A", …
$ geometry <POINT [m]> POINT (34285.07 34322.99), POINT (34382.15 34231.9), P…Below code chunk will count number of mrt station exits in each hexagon grid and assign grid_id to each station exit.
bs_count$count_train_exit = lengths(st_intersects(bs_count,train_exit))We will import the next attractiveness data, which is a kml file that contains the preschools locations.
preschool = st_read('data/geospatial/PreSchoolsLocation.kml') %>%
st_transform(crs=3414)Reading layer `PRESCHOOLS_LOCATION' from data source
`C:\jjwoo\WOO JIA JIAN (clean by Prof Kam)\Take-home_Ex2\data\geospatial\PreSchoolsLocation.kml'
using driver `KML'
Simple feature collection with 2290 features and 2 fields
Geometry type: POINT
Dimension: XYZ
Bounding box: xmin: 103.6878 ymin: 1.247759 xmax: 103.9897 ymax: 1.462134
z_range: zmin: 0 zmax: 0
Geodetic CRS: WGS 84bs_count$preschool_ct = lengths(st_intersects(bs_count,preschool))Next we will import the Business shape file, which was prepared by the course instructor. This is a potential attractiveness of destination as commuters are likely to commute via bus to work.
business = st_read(dsn='data/geospatial',
layer='Business') %>%
st_transform(crs=3414)Reading layer `Business' from data source
`C:\jjwoo\WOO JIA JIAN (clean by Prof Kam)\Take-home_Ex2\data\geospatial'
using driver `ESRI Shapefile'
Simple feature collection with 6550 features and 3 fields
Geometry type: POINT
Dimension: XY
Bounding box: xmin: 3669.148 ymin: 25408.41 xmax: 47034.83 ymax: 50148.54
Projected CRS: SVY21 / Singapore TMbs_count$business_ct = lengths(st_intersects(bs_count,business))Our final attractiveness data to be imported is the FinServ shapefile, also provided by course instructor. Financial services location is important as similar to Business, commuters are likely to travel by bus to such locations for work during weekday morning peak period.
finserv <- st_read(dsn='data/geospatial',
layer='FinServ') %>%
st_transform(crs=3414)Reading layer `FinServ' from data source
`C:\jjwoo\WOO JIA JIAN (clean by Prof Kam)\Take-home_Ex2\data\geospatial'
using driver `ESRI Shapefile'
Simple feature collection with 3320 features and 3 fields
Geometry type: POINT
Dimension: XY
Bounding box: xmin: 4881.527 ymin: 25171.88 xmax: 46526.16 ymax: 49338.02
Projected CRS: SVY21 / Singapore TMbs_count$finserv_ct = lengths(st_intersects(bs_count,finserv))Importing attractiveness and propulsiveness data (aspatial)
The first aspatial dataset to be imported is a csv file that contains locations of institutions from kindergartens to Junior Colleges.
#|eval: false
sch = read_csv('data/aspatial/Generalinformationofschools.csv')Rows: 346 Columns: 31
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (31): school_name, url_address, address, postal_code, telephone_no, tele...
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.There is a need to geocode the schools’ locations and we will use OneMap API to get the longitude and latitude using the postal_code field.
#|eval: false
url = 'https://www.onemap.gov.sg/api/common/elastic/search'
postcodes <- sch$`postal_code`
found = data.frame()
not_found = data.frame()
for(postcode in postcodes) {
query <- list('searchVal' = postcode, 'returnGeom' = 'Y', 'getAddrDetails' = 'Y', 'pageNum' = '1')
res <- GET(url, query=query)
if((content(res)$found)!=0){
found<-rbind(found, data.frame(content(res))[4:13])
} else{
not_found = data.frame(postcode)
}
}Now we will merge the output with the sch table.
#|eval: false
merged = merge(sch, found, by.x = 'postal_code' , by.y='results.POSTAL', all=TRUE)As there is one school that was not geocoded (Zhenghua Secondary School), I will output merged into a csv file and manually add in the longitude and latitude.
#|eval: false
write.csv(merged, file = 'data/aspatial/schools_geocoded.csv')Then we will reload the updated file.
sch = read_csv('data/aspatial/schools.csv') %>%
select(postal_code, school_name, results.LONGITUDE, results.LATITUDE )New names:
Rows: 350 Columns: 41
── Column specification
──────────────────────────────────────────────────────── Delimiter: "," chr
(36): postal_code, school_name, url_address, address, telephone_no, tele... dbl
(5): ...1, results.X, results.Y, results.LATITUDE, results.LONGITUDE
ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
Specify the column types or set `show_col_types = FALSE` to quiet this message.
• `` -> `...1`We will now convert sch into sf object and svy21 Singapore Projected Coordinates System.
sch_sf = st_as_sf(sch,
coords = c('results.LONGITUDE','results.LATITUDE'),
crs=4326) %>%
st_transform(crs=3414)Similar as previous section’s geospatial data, we will count number and assign grid_id.
bs_count$sch_ct = lengths(st_intersects(bs_count,sch_sf))The next aspatial dataset to be imported is hdb file, also prepared by course instructor. For data prep, we will remove non-residential data. We will also retain dwelling type info as this is a good proxy for income level. In the code chunk below, we will also use multiplier for each dwelling type, which is estimated using the HDBResidentPopulationAged15YearsandAbovebyFlatType.csv file retrieved from data.gov.
hdb = read_csv('data/aspatial/hdb.csv') %>%
filter(residential == "Y") %>%
mutate(total_rental_ct = `1room_rental`*1 + `2room_rental`*2 + `3room_rental`*3 + `other_room_rental`*4,
total_1room_ct = `1room_sold`,
total_2room_ct = `2room_sold`*2,
total_3room_ct = `3room_sold`*10,
total_4room_ct = `4room_sold`*21,
total_5room_ct = `5room_sold`*13,
total_exec_ct = `exec_sold`*4,
total_multi_ct = `multigen_sold`*13,
total_studio_ct = `studio_apartment_sold`,
total_owner_ct = `1room_sold`*1 + `2room_sold`*2 + `3room_sold`*10 + `4room_sold`*21 + `5room_sold`*13 + `exec_sold`*4 + `multigen_sold`*13 + `studio_apartment_sold`*1) %>%
select(blk_no, street, total_rental_ct, total_1room_ct, total_1room_ct,total_2room_ct,total_3room_ct,total_4room_ct,total_5room_ct,total_exec_ct,total_multi_ct, total_studio_ct,total_owner_ct, lat, lng)New names:
Rows: 12442 Columns: 37
── Column specification
──────────────────────────────────────────────────────── Delimiter: "," chr
(18): blk_no, street, residential, commercial, market_hawker, miscellane... dbl
(19): ...1, max_floor_lvl, year_completed, total_dwelling_units, 1room_s...
ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
Specify the column types or set `show_col_types = FALSE` to quiet this message.
• `` -> `...1`head(hdb, 5)# A tibble: 5 × 14
blk_no street total_rental_ct total_1room_ct total_2room_ct total_3room_ct
<chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 1 BEACH RD 0 0 2 1380
2 1 BEDOK STH… 0 0 0 2040
3 1 CHAI CHEE… 0 0 0 0
4 1 CHANGI VI… 0 0 0 540
5 1 DELTA AVE 0 0 0 0
# ℹ 8 more variables: total_4room_ct <dbl>, total_5room_ct <dbl>,
# total_exec_ct <dbl>, total_multi_ct <dbl>, total_studio_ct <dbl>,
# total_owner_ct <dbl>, lat <dbl>, lng <dbl>hdb_sf <- st_as_sf(hdb,
coords = c('lng','lat'),
crs=4326) %>%
st_transform(crs=3414)hdb_hex <- st_intersection(hdb_sf,bs_count)Warning: attribute variables are assumed to be spatially constant throughout
all geometriesBelow code chunk will group by grid_id and sum the rental count and owners count.
hdb_hex <- hdb_hex %>%
group_by(grid_id) %>%
summarise(hdb_rental_ct = sum(total_rental_ct),
hdb_1room_ct = sum(total_1room_ct),
hdb_2room_ct = sum(total_2room_ct),
hdb_3room_ct = sum(total_3room_ct),
hdb_4room_ct = sum(total_4room_ct),
hdb_5room_ct = sum(total_5room_ct),
hdb_exec_ct = sum(total_exec_ct),
hdb_multi_ct = sum(total_multi_ct),
hdb_studio_ct = sum(total_studio_ct),
hdb_owner_ct = sum (total_owner_ct)) %>%
ungroup()Once done, we can join hdb_hex back to bs_count data frame.
bs_count = st_join(bs_count, hdb_hex,
by= c('grid_id' = 'grid_id'))
bs_count<- bs_count %>%
mutate(hdb_rental_ct = replace_na(hdb_rental_ct, 0),
hdb_1room_ct = replace_na(hdb_1room_ct, 0),
hdb_2room_ct = replace_na(hdb_2room_ct, 0),
hdb_3room_ct = replace_na(hdb_3room_ct, 0),
hdb_4room_ct = replace_na(hdb_4room_ct, 0),
hdb_5room_ct = replace_na(hdb_5room_ct, 0),
hdb_exec_ct = replace_na(hdb_exec_ct, 0),
hdb_multi_ct = replace_na(hdb_multi_ct, 0),
hdb_studio_ct = replace_na(hdb_studio_ct, 0),
hdb_owner_ct = replace_na(hdb_owner_ct, 0))Prepare flow_data1 dataframe
flow_data1 will be joined by bs_count twice to include the origin and destination attributes.
bs_count$grid_id.x <- as.factor(bs_count$grid_id.x)
flow_data1 = flow_data1 %>%
left_join(bs_count,
by = c('ORIGIN_GRID_ID' = 'grid_id.x')) %>%
rename(ORIGIN_TRAIN_EXIT_CT=count_train_exit,
ORIGIN_PRESCHOOL_CT=preschool_ct,
ORIGIN_BUSINESS_CT=business_ct,
ORIGIN_FINSERV_CT=finserv_ct,
ORIGIN_SCH_CT=sch_ct,
ORIGIN_RENTAL_CT=hdb_rental_ct,
ORIGIN_1ROOM_CT=hdb_1room_ct,
ORIGIN_2ROOM_CT=hdb_2room_ct,
ORIGIN_3ROOM_CT=hdb_3room_ct,
ORIGIN_4ROOM_CT=hdb_4room_ct,
ORIGIN_5ROOM_CT=hdb_5room_ct,
ORIGIN_EXEC_CT=hdb_exec_ct,
ORIGIN_MULTI_CT=hdb_multi_ct,
ORIGIN_STUDIO_CT=hdb_studio_ct,
ORIGIN_OWNER_CT=hdb_owner_ct) %>%
select(-c(num_of_bs))flow_data1 = flow_data1 %>%
left_join(bs_count,
by = c('DESTIN_GRID_ID' = 'grid_id.x')) %>%
rename(DESTIN_TRAIN_EXIT_CT=count_train_exit,
DESTIN_PRESCHOOL_CT=preschool_ct,
DESTIN_BUSINESS_CT=business_ct,
DESTIN_FINSERV_CT=finserv_ct,
DESTIN_SCH_CT=sch_ct,
DESTIN_RENTAL_CT=hdb_rental_ct,
DESTIN_1ROOM_CT=hdb_1room_ct,
DESTIN_2ROOM_CT=hdb_2room_ct,
DESTIN_3ROOM_CT=hdb_3room_ct,
DESTIN_4ROOM_CT=hdb_4room_ct,
DESTIN_5ROOM_CT=hdb_5room_ct,
DESTIN_EXEC_CT=hdb_exec_ct,
DESTIN_MULTI_CT=hdb_multi_ct,
DESTIN_STUDIO_CT=hdb_studio_ct,
DESTIN_OWNER_CT=hdb_owner_ct) %>%
select(-c(num_of_bs))Calibrating Spatial Interaction Models (SIMs)
We will use Poisson Regression in glm() function.
Reasons for choosing Poisson Regression
Our target variable (TRIPS) is based on counts
Our target variable can only be positive values (since negative values are not meaningful)
Additional benefits include doing away with the need to abide by the assumptions of Linear Regression:
Linear relationship exist between independent and dependent variable
Residual errors follow a normal distribution,
Contain a constant variance and are not related with one another.
As a precaution, we should ensure that there are no 0 values in the variables we use.
summary(flow_data1) ORIGIN_GRID_ID DESTIN_GRID_ID TRIPS not_intra
1960 : 297 1474 : 347 Min. : 1.0 Min. : 1.0
1474 : 292 1452 : 339 1st Qu.: 7.0 1st Qu.: 7.0
1477 : 292 1516 : 326 Median : 38.0 Median : 38.0
1496 : 278 1960 : 295 Mean : 382.7 Mean : 382.7
1623 : 277 1477 : 294 3rd Qu.: 179.0 3rd Qu.: 179.0
1749 : 273 1494 : 292 Max. :96644.0 Max. :96644.0
(Other):63150 (Other):62966
dist ORIGIN_TRAIN_EXIT_CT ORIGIN_PRESCHOOL_CT ORIGIN_BUSINESS_CT
Min. : 750 Min. : 0.000 Min. : 0.000 Min. : 0.000
1st Qu.: 3000 1st Qu.: 0.000 1st Qu.: 1.000 1st Qu.: 0.000
Median : 5408 Median : 0.000 Median : 3.000 Median : 1.000
Mean : 6336 Mean : 1.247 Mean : 3.934 Mean : 6.471
3rd Qu.: 8842 3rd Qu.: 2.000 3rd Qu.: 6.000 3rd Qu.: 6.000
Max. :24784 Max. :13.000 Max. :21.000 Max. :97.000
ORIGIN_FINSERV_CT ORIGIN_SCH_CT grid_id.y.x ORIGIN_RENTAL_CT
Min. : 0.000 Min. :0.0000 Min. : 449 Min. : 0.0
1st Qu.: 1.000 1st Qu.:0.0000 1st Qu.:1180 1st Qu.: 0.0
Median : 3.000 Median :0.0000 Median :1477 Median : 0.0
Mean : 7.918 Mean :0.5603 Mean :1452 Mean : 195.9
3rd Qu.: 9.000 3rd Qu.:1.0000 3rd Qu.:1732 3rd Qu.: 21.0
Max. :139.000 Max. :4.0000 Max. :2343 Max. :2893.0
NA's :22951
ORIGIN_1ROOM_CT ORIGIN_2ROOM_CT ORIGIN_3ROOM_CT ORIGIN_4ROOM_CT
Min. : 0.0000 Min. : 0.00 Min. : 0 Min. : 0
1st Qu.: 0.0000 1st Qu.: 0.00 1st Qu.: 0 1st Qu.: 0
Median : 0.0000 Median : 0.00 Median : 870 Median : 5628
Mean : 0.3656 Mean : 76.99 Mean : 4815 Mean :14843
3rd Qu.: 0.0000 3rd Qu.: 42.00 3rd Qu.: 6470 3rd Qu.:25599
Max. :255.0000 Max. :4164.00 Max. :40960 Max. :99162
ORIGIN_5ROOM_CT ORIGIN_EXEC_CT ORIGIN_MULTI_CT ORIGIN_STUDIO_CT
Min. : 0 Min. : 0.0 Min. : 0.000 Min. : 0.0
1st Qu.: 0 1st Qu.: 0.0 1st Qu.: 0.000 1st Qu.: 0.0
Median : 1885 Median : 0.0 Median : 0.000 Median : 0.0
Mean : 5029 Mean : 414.7 Mean : 6.848 Mean : 14.3
3rd Qu.: 7462 3rd Qu.: 388.0 3rd Qu.: 0.000 3rd Qu.: 0.0
Max. :61945 Max. :6776.0 Max. :1976.000 Max. :252.0
ORIGIN_OWNER_CT area_hex_grid.x DESTIN_TRAIN_EXIT_CT
Min. : 0 POLYGON :64859 Min. : 0.000
1st Qu.: 0 epsg:3414 : 0 1st Qu.: 0.000
Median : 11321 +proj=tmer...: 0 Median : 0.000
Mean : 25200 Mean : 1.341
3rd Qu.: 46754 3rd Qu.: 2.000
Max. :130585 Max. :13.000
DESTIN_PRESCHOOL_CT DESTIN_BUSINESS_CT DESTIN_FINSERV_CT DESTIN_SCH_CT
Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. :0.0000
1st Qu.: 1.000 1st Qu.: 0.000 1st Qu.: 1.000 1st Qu.:0.0000
Median : 3.000 Median : 1.000 Median : 3.000 Median :0.0000
Mean : 3.777 Mean : 7.303 Mean : 8.604 Mean :0.5293
3rd Qu.: 6.000 3rd Qu.: 6.000 3rd Qu.: 9.000 3rd Qu.:1.0000
Max. :21.000 Max. :97.000 Max. :139.000 Max. :4.0000
grid_id.y.y DESTIN_RENTAL_CT DESTIN_1ROOM_CT DESTIN_2ROOM_CT
Min. : 449 Min. : 0.0 Min. : 0.0000 Min. : 0.00
1st Qu.:1181 1st Qu.: 0.0 1st Qu.: 0.0000 1st Qu.: 0.00
Median :1478 Median : 0.0 Median : 0.0000 Median : 0.00
Mean :1457 Mean : 188.5 Mean : 0.3814 Mean : 71.73
3rd Qu.:1731 3rd Qu.: 15.0 3rd Qu.: 0.0000 3rd Qu.: 16.00
Max. :2343 Max. :2893.0 Max. :255.0000 Max. :4164.00
NA's :24599
DESTIN_3ROOM_CT DESTIN_4ROOM_CT DESTIN_5ROOM_CT DESTIN_EXEC_CT
Min. : 0 Min. : 0 Min. : 0 Min. : 0.0
1st Qu.: 0 1st Qu.: 0 1st Qu.: 0 1st Qu.: 0.0
Median : 660 Median : 4452 Median : 1092 Median : 0.0
Mean : 4547 Mean :13809 Mean : 4629 Mean : 375.8
3rd Qu.: 6140 3rd Qu.:23961 3rd Qu.: 6877 3rd Qu.: 288.0
Max. :40960 Max. :99162 Max. :61945 Max. :6776.0
DESTIN_MULTI_CT DESTIN_STUDIO_CT DESTIN_OWNER_CT area_hex_grid.y
Min. : 0.000 Min. : 0.00 Min. : 0 POLYGON :64859
1st Qu.: 0.000 1st Qu.: 0.00 1st Qu.: 0 epsg:3414 : 0
Median : 0.000 Median : 0.00 Median : 9446 +proj=tmer...: 0
Mean : 6.767 Mean : 13.22 Mean : 23454
3rd Qu.: 0.000 3rd Qu.: 0.00 3rd Qu.: 42764
Max. :1976.000 Max. :252.00 Max. :130585
Code chunk below is to replace 0 values to 0.99 (arbitrary value near 0)
flow_data1$ORIGIN_TRAIN_EXIT_CT = ifelse(
flow_data1$ORIGIN_TRAIN_EXIT_CT == 0,
0.99, flow_data1$ORIGIN_TRAIN_EXIT_CT)
flow_data1$ORIGIN_PRESCHOOL_CT = ifelse(
flow_data1$ORIGIN_PRESCHOOL_CT == 0,
0.99, flow_data1$ORIGIN_PRESCHOOL_CT)
flow_data1$ORIGIN_BUSINESS_CT = ifelse(
flow_data1$ORIGIN_BUSINESS_CT == 0,
0.99, flow_data1$ORIGIN_BUSINESS_CT)
flow_data1$ORIGIN_FINSERV_CT = ifelse(
flow_data1$ORIGIN_FINSERV_CT == 0,
0.99, flow_data1$ORIGIN_FINSERV_CT)
flow_data1$ORIGIN_SCH_CT = ifelse(
flow_data1$ORIGIN_SCH_CT == 0,
0.99, flow_data1$ORIGIN_SCH_CT)
flow_data1$ORIGIN_RENTAL_CT = ifelse(
flow_data1$ORIGIN_RENTAL_CT == 0,
0.99, flow_data1$ORIGIN_RENTAL_CT)
flow_data1$ORIGIN_1ROOM_CT = ifelse(
flow_data1$ORIGIN_1ROOM_CT == 0,
0.99, flow_data1$ORIGIN_1ROOM_CT)
flow_data1$ORIGIN_2ROOM_CT = ifelse(
flow_data1$ORIGIN_2ROOM_CT == 0,
0.99, flow_data1$ORIGIN_2ROOM_CT)
flow_data1$ORIGIN_3ROOM_CT = ifelse(
flow_data1$ORIGIN_3ROOM_CT == 0,
0.99, flow_data1$ORIGIN_3ROOM_CT)
flow_data1$ORIGIN_4ROOM_CT = ifelse(
flow_data1$ORIGIN_4ROOM_CT == 0,
0.99, flow_data1$ORIGIN_4ROOM_CT)
flow_data1$ORIGIN_5ROOM_CT = ifelse(
flow_data1$ORIGIN_5ROOM_CT == 0,
0.99, flow_data1$ORIGIN_5ROOM_CT)
flow_data1$ORIGIN_EXEC_CT = ifelse(
flow_data1$ORIGIN_EXEC_CT == 0,
0.99, flow_data1$ORIGIN_EXEC_CT)
flow_data1$ORIGIN_MULTI_CT = ifelse(
flow_data1$ORIGIN_MULTI_CT == 0,
0.99, flow_data1$ORIGIN_MULTI_CT)
flow_data1$ORIGIN_STUDIO_CT = ifelse(
flow_data1$ORIGIN_STUDIO_CT == 0,
0.99, flow_data1$ORIGIN_STUDIO_CT)
flow_data1$ORIGIN_OWNER_CT = ifelse(
flow_data1$ORIGIN_OWNER_CT == 0,
0.99, flow_data1$ORIGIN_OWNER_CT)
flow_data1$DESTIN_TRAIN_EXIT_CT = ifelse(
flow_data1$DESTIN_TRAIN_EXIT_CT == 0,
0.99, flow_data1$DESTIN_TRAIN_EXIT_CT)
flow_data1$DESTIN_PRESCHOOL_CT = ifelse(
flow_data1$DESTIN_PRESCHOOL_CT == 0,
0.99, flow_data1$DESTIN_PRESCHOOL_CT)
flow_data1$DESTIN_BUSINESS_CT = ifelse(
flow_data1$DESTIN_BUSINESS_CT == 0,
0.99, flow_data1$DESTIN_BUSINESS_CT)
flow_data1$DESTIN_FINSERV_CT = ifelse(
flow_data1$DESTIN_FINSERV_CT == 0,
0.99, flow_data1$DESTIN_FINSERV_CT)
flow_data1$DESTIN_SCH_CT = ifelse(
flow_data1$DESTIN_SCH_CT == 0,
0.99, flow_data1$DESTIN_SCH_CT)
flow_data1$DESTIN_RENTAL_CT = ifelse(
flow_data1$DESTIN_RENTAL_CT == 0,
0.99, flow_data1$DESTIN_RENTAL_CT)
flow_data1$DESTIN_1ROOM_CT = ifelse(
flow_data1$DESTIN_1ROOM_CT == 0,
0.99, flow_data1$DESTIN_1ROOM_CT)
flow_data1$DESTIN_2ROOM_CT = ifelse(
flow_data1$DESTIN_2ROOM_CT == 0,
0.99, flow_data1$DESTIN_2ROOM_CT)
flow_data1$DESTIN_3ROOM_CT = ifelse(
flow_data1$DESTIN_3ROOM_CT == 0,
0.99, flow_data1$DESTIN_3ROOM_CT)
flow_data1$DESTIN_4ROOM_CT = ifelse(
flow_data1$DESTIN_4ROOM_CT == 0,
0.99, flow_data1$DESTIN_4ROOM_CT)
flow_data1$DESTIN_5ROOM_CT = ifelse(
flow_data1$DESTIN_5ROOM_CT == 0,
0.99, flow_data1$DESTIN_5ROOM_CT)
flow_data1$DESTIN_EXEC_CT = ifelse(
flow_data1$DESTIN_EXEC_CT == 0,
0.99, flow_data1$DESTIN_EXEC_CT)
flow_data1$DESTIN_MULTI_CT = ifelse(
flow_data1$DESTIN_MULTI_CT == 0,
0.99, flow_data1$DESTIN_MULTI_CT)
flow_data1$DESTIN_STUDIO_CT = ifelse(
flow_data1$DESTIN_STUDIO_CT == 0,
0.99, flow_data1$DESTIN_STUDIO_CT)
flow_data1$DESTIN_OWNER_CT = ifelse(
flow_data1$DESTIN_OWNER_CT == 0,
0.99, flow_data1$DESTIN_OWNER_CT)summary(flow_data1) ORIGIN_GRID_ID DESTIN_GRID_ID TRIPS not_intra
1960 : 297 1474 : 347 Min. : 1.0 Min. : 1.0
1474 : 292 1452 : 339 1st Qu.: 7.0 1st Qu.: 7.0
1477 : 292 1516 : 326 Median : 38.0 Median : 38.0
1496 : 278 1960 : 295 Mean : 382.7 Mean : 382.7
1623 : 277 1477 : 294 3rd Qu.: 179.0 3rd Qu.: 179.0
1749 : 273 1494 : 292 Max. :96644.0 Max. :96644.0
(Other):63150 (Other):62966
dist ORIGIN_TRAIN_EXIT_CT ORIGIN_PRESCHOOL_CT ORIGIN_BUSINESS_CT
Min. : 750 Min. : 0.990 Min. : 0.990 Min. : 0.990
1st Qu.: 3000 1st Qu.: 0.990 1st Qu.: 1.000 1st Qu.: 0.990
Median : 5408 Median : 0.990 Median : 3.000 Median : 1.000
Mean : 6336 Mean : 1.875 Mean : 4.111 Mean : 6.918
3rd Qu.: 8842 3rd Qu.: 2.000 3rd Qu.: 6.000 3rd Qu.: 6.000
Max. :24784 Max. :13.000 Max. :21.000 Max. :97.000
ORIGIN_FINSERV_CT ORIGIN_SCH_CT grid_id.y.x ORIGIN_RENTAL_CT
Min. : 0.990 Min. :0.990 Min. : 449 Min. : 0.99
1st Qu.: 1.000 1st Qu.:0.990 1st Qu.:1180 1st Qu.: 0.99
Median : 3.000 Median :0.990 Median :1477 Median : 0.99
Mean : 8.152 Mean :1.182 Mean :1452 Mean : 196.52
3rd Qu.: 9.000 3rd Qu.:1.000 3rd Qu.:1732 3rd Qu.: 21.00
Max. :139.000 Max. :4.000 Max. :2343 Max. :2893.00
NA's :22951
ORIGIN_1ROOM_CT ORIGIN_2ROOM_CT ORIGIN_3ROOM_CT ORIGIN_4ROOM_CT
Min. : 0.990 Min. : 0.99 Min. : 0.99 Min. : 0.99
1st Qu.: 0.990 1st Qu.: 0.99 1st Qu.: 0.99 1st Qu.: 0.99
Median : 0.990 Median : 0.99 Median : 870.00 Median : 5628.00
Mean : 1.354 Mean : 77.71 Mean : 4815.78 Mean :14843.56
3rd Qu.: 0.990 3rd Qu.: 42.00 3rd Qu.: 6470.00 3rd Qu.:25599.00
Max. :255.000 Max. :4164.00 Max. :40960.00 Max. :99162.00
ORIGIN_5ROOM_CT ORIGIN_EXEC_CT ORIGIN_MULTI_CT ORIGIN_STUDIO_CT
Min. : 0.99 Min. : 0.99 Min. : 0.990 Min. : 0.99
1st Qu.: 0.99 1st Qu.: 0.99 1st Qu.: 0.990 1st Qu.: 0.99
Median : 1885.00 Median : 0.99 Median : 0.990 Median : 0.99
Mean : 5028.96 Mean : 415.32 Mean : 7.827 Mean : 15.17
3rd Qu.: 7462.00 3rd Qu.: 388.00 3rd Qu.: 0.990 3rd Qu.: 0.99
Max. :61945.00 Max. :6776.00 Max. :1976.000 Max. :252.00
ORIGIN_OWNER_CT area_hex_grid.x DESTIN_TRAIN_EXIT_CT
Min. : 0.99 POLYGON :64859 Min. : 0.990
1st Qu.: 0.99 epsg:3414 : 0 1st Qu.: 0.990
Median : 11321.00 +proj=tmer...: 0 Median : 0.990
Mean : 25200.71 Mean : 1.963
3rd Qu.: 46754.00 3rd Qu.: 2.000
Max. :130585.00 Max. :13.000
DESTIN_PRESCHOOL_CT DESTIN_BUSINESS_CT DESTIN_FINSERV_CT DESTIN_SCH_CT
Min. : 0.990 Min. : 0.990 Min. : 0.990 Min. :0.990
1st Qu.: 1.000 1st Qu.: 0.990 1st Qu.: 1.000 1st Qu.:0.990
Median : 3.000 Median : 1.000 Median : 3.000 Median :0.990
Mean : 3.967 Mean : 7.731 Mean : 8.842 Mean :1.171
3rd Qu.: 6.000 3rd Qu.: 6.000 3rd Qu.: 9.000 3rd Qu.:1.000
Max. :21.000 Max. :97.000 Max. :139.000 Max. :4.000
grid_id.y.y DESTIN_RENTAL_CT DESTIN_1ROOM_CT DESTIN_2ROOM_CT
Min. : 449 Min. : 0.99 Min. : 0.99 Min. : 0.99
1st Qu.:1181 1st Qu.: 0.99 1st Qu.: 0.99 1st Qu.: 0.99
Median :1478 Median : 0.99 Median : 0.99 Median : 0.99
Mean :1457 Mean : 189.19 Mean : 1.37 Mean : 72.46
3rd Qu.:1731 3rd Qu.: 15.00 3rd Qu.: 0.99 3rd Qu.: 16.00
Max. :2343 Max. :2893.00 Max. :255.00 Max. :4164.00
NA's :24599
DESTIN_3ROOM_CT DESTIN_4ROOM_CT DESTIN_5ROOM_CT DESTIN_EXEC_CT
Min. : 0.99 Min. : 0.99 Min. : 0.99 Min. : 0.99
1st Qu.: 0.99 1st Qu.: 0.99 1st Qu.: 0.99 1st Qu.: 0.99
Median : 660.00 Median : 4452.00 Median : 1092.00 Median : 0.99
Mean : 4547.51 Mean :13809.70 Mean : 4629.70 Mean : 376.42
3rd Qu.: 6140.00 3rd Qu.:23961.00 3rd Qu.: 6877.00 3rd Qu.: 288.00
Max. :40960.00 Max. :99162.00 Max. :61945.00 Max. :6776.00
DESTIN_MULTI_CT DESTIN_STUDIO_CT DESTIN_OWNER_CT area_hex_grid.y
Min. : 0.990 Min. : 0.99 Min. : 0.99 POLYGON :64859
1st Qu.: 0.990 1st Qu.: 0.99 1st Qu.: 0.99 epsg:3414 : 0
Median : 0.990 Median : 0.99 Median : 9446.00 +proj=tmer...: 0
Mean : 7.746 Mean : 14.10 Mean : 23453.93
3rd Qu.: 0.990 3rd Qu.: 0.99 3rd Qu.: 42764.00
Max. :1976.000 Max. :252.00 Max. :130585.00
We can see that there are no more 0 values.
Building the SIMs
We have the following attractiveness and propulsiveness variables:
Attractiveness: ‘DESTIN_TRAIN_EXIT_CT’, ‘DESTIN_PRESCHOOL_CT’, ‘DESTIN_BUSINESS_CT’, ‘DESTIN_FINSERV_CT’, ‘DESTIN_SCH_CT’
Propulsiveness: ‘ORIGIN_RENTAL_CT’, ‘ORIGIN_nROOM_CT’
Unconstrained SIM
uncSIM = glm(formula = TRIPS ~
log(ORIGIN_RENTAL_CT) +
log(ORIGIN_2ROOM_CT) +
log(ORIGIN_3ROOM_CT) +
log(ORIGIN_4ROOM_CT) +
log(ORIGIN_5ROOM_CT) +
log(ORIGIN_EXEC_CT) +
log(ORIGIN_STUDIO_CT) +
log(DESTIN_TRAIN_EXIT_CT) +
log(DESTIN_PRESCHOOL_CT) +
log(DESTIN_BUSINESS_CT) +
log(DESTIN_FINSERV_CT) +
log(DESTIN_SCH_CT) +
log(dist),
family = poisson(link = "log"),
data = flow_data1,
na.action = na.exclude)Constrained SIM - Origin
orcSIM <- glm(formula= TRIPS ~
ORIGIN_GRID_ID +
log(DESTIN_TRAIN_EXIT_CT) +
log(DESTIN_PRESCHOOL_CT) +
log(DESTIN_BUSINESS_CT) +
log(DESTIN_FINSERV_CT) +
log(DESTIN_SCH_CT) +
log(dist)-1,
family = poisson(link='log'),
data = flow_data1,
na.action = na.exclude)Constrained SIM - Destination
decSIM <- glm(formula= TRIPS ~
DESTIN_GRID_ID +
log(ORIGIN_RENTAL_CT) +
log(ORIGIN_2ROOM_CT) +
log(ORIGIN_3ROOM_CT) +
log(ORIGIN_4ROOM_CT) +
log(ORIGIN_5ROOM_CT) +
log(ORIGIN_EXEC_CT) +
log(ORIGIN_STUDIO_CT) +
log(dist)-1,
family = poisson(link='log'),
data = flow_data1,
na.action = na.exclude)Analysis of SIMs output
The goodness-of-fit test using r-square values is used to evaluate how well the models explain variations in number of O-D trips.
Unconstrained SIM output R-square
r = cor(uncSIM$data$TRIPS, uncSIM$fitted.values)
uncSIM_r2 <- r^2
uncSIM_r2[1] 0.2539203Constrained SIM - Origin output R-square
r = cor(orcSIM$data$TRIPS, orcSIM$fitted.values)
orcSIM_r2 <- r^2
orcSIM_r2[1] 0.3685562Constrained SIM - Destination output R-square
r = cor(decSIM$data$TRIPS, decSIM$fitted.values)
decSIM_r2 <- r^2
decSIM_r2[1] 0.3646562Model Comparison
The compare_performance() of performance package is used to compare the root mean square error (RMSE) of the 3 SIMs. The model with the smallest RMSE is the best.
We will first create a list model_list by using the code chunk below and it will have all the fitted models for all 3 variations of gravity model.
model_list = list(unconstrained=uncSIM,
originConstrained=orcSIM,
destinationConstrained=decSIM)compare_performance(model_list,
metrics = "RMSE")# Comparison of Model Performance Indices
Name | Model | RMSE
-----------------------------------------
unconstrained | glm | 1630.481
originConstrained | glm | 1498.418
destinationConstrained | glm | 1503.742From the output, we can see that origin constrained model is the best performing model with RMSE value of 1498.418
Visualisation of fitted values
We will append all fitted values of the SIMs into flow_data1 with below code chunk using a function.
append_fitted_values <- function(data, fitted_values, new_column_name) {
df <- as.data.frame(fitted_values) %>%
round(digits = 0)
data <- data %>%
cbind(df) %>%
rename({{ new_column_name }} := fitted_values)
return(data)
}flow_data1 <- append_fitted_values(flow_data1, uncSIM$fitted.values, "uncTRIPS")
flow_data1 <- append_fitted_values(flow_data1, orcSIM$fitted.values, "orcTRIPS")
flow_data1 <- append_fitted_values(flow_data1, decSIM$fitted.values, "decTRIPS")We now plot the actual and predicted values for better visualisation.
unc_p <- ggplot(data = flow_data1,
aes(x = uncTRIPS,
y = TRIPS)) +
geom_point(size = 0.5) +
geom_smooth(method = lm)
orc_p <- ggplot(data = flow_data1,
aes(x = orcTRIPS,
y = TRIPS)) +
geom_point(size = 0.5) +
geom_smooth(method = lm)
dec_p <- ggplot(data = flow_data1,
aes(x = decTRIPS,
y = TRIPS)) +
geom_point(size = 0.5) +
geom_smooth(method = lm)
ggarrange(unc_p, orc_p, dec_p,
ncol = 2,
nrow = 2)`geom_smooth()` using formula = 'y ~ x'
`geom_smooth()` using formula = 'y ~ x'
`geom_smooth()` using formula = 'y ~ x'
The scatterplots with original values above show that there is a stronger linear trend between fitted and observed values in the unconstrained model. However, due to the skewed nature of the distribution of trips, this may result in disproprotional or unmeaningful conclusions. In order to stabilize the variance of the data scales, we shall now look at log transformed fitted versus observed trip values.
log_unc <- ggplot(
data = flow_data1,
aes(x = log(uncTRIPS),
y = log(TRIPS))
) +
geom_point(
size = flow_data1$TRIPS/10000,
alpha = .6
) +
geom_smooth(
method = lm,
se = TRUE
) +
labs(title = "Log(Unconstrained)") +
theme(
plot.title = element_text(size = 10),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.ticks.x = element_blank()
)
log_unc`geom_smooth()` using formula = 'y ~ x'
log_orc <- ggplot(
data = flow_data1,
aes(x = log(orcTRIPS),
y = log(TRIPS))
) +
geom_point(
size = flow_data1$TRIPS/10000,
color = "#4d5887",
alpha = .6
) +
geom_smooth(
method = lm,
se = TRUE
) +
labs(title = "Log(Origin-constrained)") +
theme(
plot.title = element_text(size = 10),
axis.text.y = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
axis.title.y = element_blank()
)
log_orc`geom_smooth()` using formula = 'y ~ x'Warning: Removed 37 rows containing non-finite values (`stat_smooth()`).
log_dec <- ggplot(
data = flow_data1,
aes(x = log(decTRIPS),
y = log(TRIPS))
) +
geom_point(
size = flow_data1$TRIPS/10000,
color = "#6D435A",
alpha = .6
) +
geom_smooth(
method = lm,
se = TRUE
) +
labs(title = "Log(Destination-constrained)") +
theme(
plot.title = element_text(size = 10),
axis.text.y = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
axis.title.y = element_blank()
)
log_dec`geom_smooth()` using formula = 'y ~ x'Warning: Removed 18 rows containing non-finite values (`stat_smooth()`).
The log-transformed plots seem to provide an easier interpretation of the values, and origin-constrained model seems to have a higher linear relationship compared to the other models.
Key Takeaways
We have built 3 SIMs and observe that origin constrained model is the best performing model. This might suggest that the simplicity of the model is effective in capturing underlying patterns in the data. Even though it has fewer variables compared to the other two models, the origin constrained model achieved better performance – thus indicating that the additional complexity of the other models may not necessarily improve the explanatory power of the models.
The explanatory variables used for the SIMs are related to the quantity of specific types of facilities, but does not account for the quality of these features. More qualitative data such as categories of retail shops or customer traffic of F&B venues could be potential factors to be used in the future.
There are 2 limitations of the exercise. Firstly the Passenger Volume by Origin Destination Bus Stops data used do not included any transfer trip information. With our attractive and propulsive attributes, we can only assume that passengers take only 1 bus trip from source to their intended destination. With the inclusion of the MRT train station exits locations, we factor in the possibility of passenger commuting to MRT station for transfer.
Secondly, the population density distribution of Residents could only be estimated from the HDB.csv and not directly taken from the Resident Distribution by single age and subzone dataset because we have reduced our basic spatial unit to 750m hexagons instead of subzone (to reduce social-economic and spatial aggregation errors). Now, each hexagon could have a mix of 7 subzones inside it, thus use of Resident Distribution by single age and subzone dataset is inappropriate (otherwise we could have achieved better results).